Comparison of the two prophylactic HPV vaccines, Gardasil® and Cervarix®.
\r\n\tThe purpose of this book is to discuss some of the critical security challenges in today’s computing world and to discuss mechanisms for defending against those attacks by using classical and modern approaches to cryptography and other security solutions. With this objective, the book invites contributions from researchers in the field of cryptography and its applications in network security. Some illustrative topics of interest (but not limited to) are cryptography algorithms, authentication, authorization, integrity, confidentiality, privacy, security in wireless networks, security in wireless local area networks, wireless sensor networks, wireless ad hoc networks, vehicular ad hoc networks, security and privacy in the Internet of Things. Privacy of information, Blockchains, and Machine Learning in Security are three additional topics that the book will also deal with.
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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"44324",title:"Human Papillomavirus Prophylactic Vaccines and Alternative Strategies for Prevention",doi:"10.5772/55852",slug:"human-papillomavirus-prophylactic-vaccines-and-alternative-strategies-for-prevention",body:'First evidence that transmissible agents are involved in the development of cervical cancer dates back to the mid 19th century and is based on investigations of the Italian physician Demonico Rigoni-Stern who recognized that cancer of the womb is found most frequently among women in their fourth and fifth decade and that factors such as age of sexual debut and promiscuity attribute to the risk of acquiring this type of cancer [1]. However, only with the advent of molecular biology in the early 1970s, and after ruling out Herpes Simplex Viruses, a link was established between cervical cancer and infections by certain types of human papillomaviruses (HPV). After isolation of HPV 6 from a condyloma and subsequently of HPV 11 from a laryngeal papilloma the genomic DNA of these two types allowed tracing of other, novel HPVs in biopsies of cervical tumors [2, 3]. The detection of HPV DNA in tumor cells, including the HeLa cell line, was initially met with much doubt and disbelief in the scientific community but could subsequently be confirmed. In fact, the initial observation by Dürst et al. [4] that 11 out of 18 cervical cancer biopsies from German patients were positive for HPV 16 is consistent with today’s knowledge of HPV 16 being present in more than 50% of malignant tumors from the cervix. In the following years the findings by Harald zur Hausen and his colleagues were confirmed by numerous laboratories worldwide and a causative link between HPV infections and cervical cancer in humans was established due to the vast amount of epidemiological studies and an overwhelming body of data obtained in different
The first observations in respect to therapeutic or prophylactic vaccination against papillomaviruses (PV) were made using models of experimental induction of warts in rabbits and humans. In heroic and bold self-experimentation Findlay inoculated himself with wart extracts and noted that he became ‘immune’ to wart induction. Similarly, Grigg and Wilhelm noted patterns for the appearance of skin warts in school children and attributed their findings to a possible ‘resistance’ of some individuals [5]. In the first half of the last century a number of efforts were undertaken to treat skin and genital warts by the injection of autologous and heterologous wart extracts; some of these attempts were seemingly met with success [6].
A systematic development of prophylactic papillomavirus vaccines proved difficult without a virus that can be replicated in culture, suitable animal models, and markers for protection. Still, a number of prophylactic vaccine approaches were performed either by the use of formalin-fixed wart extracts or by inactivated purified viruses e.g. in dogs, rabbits, cattle and horses (for review see: [7]). By passive transfer Chambers et al. demonstrated that antibodies confer protection against induction of oral papillomas [8]. One of the first
In recent years, the so called pseudovirion-based neutralization assays (PBNAs) have been regarded as the gold standard for the detection of neutralizing antibodies against PVs [12]. These assays have in common that a plasmid encoding a reporter gene (such as secreted alkaline phosphatase, luciferases, fluorescent proteins) is encapsidated in mammalian cells by expression of codon-optimized L1 and L2 genes (Fig. 1). These pseudoviruses can be purified e.g. by gradient centrifugation and used to infect cells
Pseudovirion-based neutralization assay (PBNA). Gaussia = Gaussia luciferase; GFP: green fluorescent protein; SV40 ORI = SV40 origin for replication. Pseudovirions (PSV) encapsidating a Gaussia luciferase reporter gene were produced in mammalian cells and used for infection of HeLa cells. The levels of secreted Gaussia (light blue arrows) can be quantified by a luminescence assay. The presence of neutralizing antibodies (dark blue) abrogates PSV infection and the subsequent secretion of Gaussia.
PV pseudovirions have also been used in a cervicovaginal mouse model for the detection of neutralizing antibodies. In this model, the female mouse genital epithelium is infected with pseudovirions carrying a firefly luciferase gene and luciferase activity is monitored by
Many years of research showing that anti-L1 antibodies protect against HPV infection and L1 can assemble into particles called virus-like particles culminated and [14] triggered the development of the current HPV vaccines [15].
Two commercially available prophylactic HPV vaccines, Cervarix® (GSK) and Gardasil® (Merck) have been licensed in over 100 countries. Both are composed of the L1 major capsid protein assembled into non-infectious and highly immunogenic virus-like-particles (VLPs) [16].
Cervarix® is a bivalent vaccine containing VLPs from the two most prevalent high-risk HPV types 16 and 18. The VLPs are produced in insect cells and formulated with the adjuvant system AS04 (composed of aluminium hydroxyphosphate sulfate combined with MPL- 3-O-deacyl-4’-monophosphoryl lipid A) [17]. Gardasil® is a quadrivalent vaccine that in addition to HPV16 and HPV18 VLPs also contains HPV6 and HPV11 VLPs. These two low-risk types are responsible for nearly 90% of the genital warts. The VLPs in Gardasil® are produced in a yeast system and adjuvanted with aluminium hydroxiphosphate sulfate salt [18] (Table 1).
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Producer cells | \n\t\t\tYeast Saccharomyces cerevisiae CANADE 3C-5 (Stamm 1895) | \n\t\t\tInsect cells Spodoptera frugiperda Sf-9, Trichoplusia ni Hi-5 | \n\t\t
Antigen | \n\t\t\t20 µg HPV6 L1 VLP 40 µg HPV11 L1 VLP 40 µg HPV16 L1 VLP 20 µg HPV 18 L1VLP | \n\t\t\t20 µg HPV16 L1 VLP 20 µg HPV 18 L1VLP | \n\t\t
Vaccination schedule | \n\t\t\tMonths 0, 2, 6 | \n\t\t\tMonths 0, 1, 6 | \n\t\t
Package | \n\t\t\tReady-to-use syringe Ampules 0.5 mL | \n\t\t\tAmpules 0.5 mL | \n\t\t
Vaccine recommendation (ACIP) | \n\t\t\tVaccination of female at age 11 or 12 years (catch-up: 13-26 years old). Vaccination of male aged 9 through 26 years. | \n\t\t\tVaccination of female aged 11 or 12 years old (can be started at 9 years ). | \n\t\t
Since the main target groups for the HPV vaccines are children and young women that have not initiated sexual activity, safety was the highest priority for the two vaccine producers.
Over the past years many studies have been conducted to ensure safety and tolerability of Cervarix® and Gardasil® [21, 22]. Independent of age, sex or ethnicity, the HPV vaccines are highly safe and well tolerated with very little adverse effects and no significant differences between Gardasil® and Cervarix®. However, in a direct comparison study between the two vaccines, Cervarix® was associated with higher rates of local injection site reactions than Gardasil® [23] (Table 2). This effect might be associated with the differences in adjuvant formulation between the two vaccines.
The most common adverse effects for both vaccines are pain, reddening and swelling at the site of the injection as well as syncope, fatigue, nausea, dizziness and migraine. No severe side effects including auto-immune response abortion or abnormal pregnancy were observed with increased frequency after vaccination with Cervarix® or Gardasil® when compared to the control groups [24-26]
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Pain | \n\t\t\t92.9 [90.4, 95.0] | \n\t\t\t71.6 [67.5, 75.4] | \n\t\t
Redness | \n\t\t\t44.3 [40.0, 48.6] | \n\t\t\t25.6 [21.9, 29.5] | \n\t\t
Swelling | \n\t\t\t36.5 [32.3, 40.7] | \n\t\t\t21.8 [18.3, 25.5] | \n\t\t
Fatigue | \n\t\t\t49.8 [45.5, 54.2] | \n\t\t\t39.8 [35.6, 44.1] | \n\t\t
Headache | \n\t\t\t47.5 [43.2, 51.9] | \n\t\t\t41.9 [37.6, 46.3] | \n\t\t
Fever ≥ 39.0 °C | \n\t\t\t0.4 [0.0, 1.4] | \n\t\t\t0.0 [0.0, 0.7] | \n\t\t
As HPV infection is limited to basal epithelial cells, the virus is normally “hiding” from circulating immune cells during initial stages of infection, limiting the host’s immune responses. Additionally, to evade the host’s immune system and achieve persistent infection, HPV has developed several mechanisms to down-regulate host immunity [27, 28]. The virus’s success in evading the immune system is corroborated by the finding that of the women infected with HPV, only 50% develop anti-HPV antibodies (mainly anti-L1). Whether these antibodies can protect against re-incident infection remains unclear.
The mechanisms of immunity induced by the HPV vaccines are not fully understood but it seems that humoral immunity (virus-specific neutralizing immunoglobulin G antibodies) plays an important role. Passive transfer of immune serum in pre-clinical animal models, for example, have demonstrated that L1 virus-specific antibodies are sufficient to prevent papillomavirus infection [14, 29, 30].
Cervarix® and Gardasil® induce production of high levels of anti-L1 antibodies that reach their peak seven months after the administration of the third dose. The level of antibodies gradually decreases over time but even after several years the titers remain higher than in naturally infected women.
Both vaccines lead to seroconversion of nearly 100% of the immunized subjects. Cervarix® was shown to sustain relatively stable immunity against HPV16/18 for more than eight years [31]. Subjects immunized with Gardasil® were shown to be consistently seropositive for more than four years for HPV11, HPV6 and HPV16 but a decline in antibody titers was recorded for HPV18 (from 100% to approximately 47%) [32]. However, it cannot be excluded that this observed decline is a result of assay insensitivity. Nevertheless, the protection against HPV18 induced lesions did not decrease suggesting that low levels of anti-HPV18 antibodies are sufficient to confer protection. The Table 3 shows the efficacy of Cervarix® and Gardasil® for different clinical trials followed up for different periods of time.
\n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t\t | \n\t\t\t\n\t\t | |||
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Cervarix® | \n\t\t\t1.5 | \n\t\t\tAccording -to -protocol | \n\t\t\t100% | \n\t\t\tN/A | \n\t\t\t− | \n\t\t\t− | \n\t\t\t[33] | \n\t\t
3 | \n\t\t\tAccording -to -protocol | \n\t\t\t− | \n\t\t\t− | \n\t\t\t98.1% | \n\t\t\t100% | \n\t\t\t[34] | \n\t\t|
4.5 | \n\t\t\tAccording -to -protocol | \n\t\t\t100% | \n\t\t\t100% | \n\t\t\t100% | \n\t\t\t− | \n\t\t\t[35] | \n\t\t|
5.5 | \n\t\t\tAccording -to -protocol | \n\t\t\t100% | \n\t\t\t100% | \n\t\t\t100% | \n\t\t\t− | \n\t\t\t[36] | \n\t\t|
7.3 | \n\t\t\tAccording -to -protocol | \n\t\t\t100% | \n\t\t\t100% | \n\t\t\t100% | \n\t\t\t− | \n\t\t\t[37] | \n\t\t|
8.4 | \n\t\t\tAccording -to -protocol | \n\t\t\t100% | \n\t\t\t100% | \n\t\t\t100% | \n\t\t\t− | \n\t\t\t[38] | \n\t\t|
Gardasil® | \n\t\t\t3 | \n\t\t\tPer protocol susceptible population | \n\t\t\t− | \n\t\t\t− | \n\t\t\t100% | \n\t\t\t97% | \n\t\t\t[39] | \n\t\t
3 | \n\t\t\tPer protocol susceptible population | \n\t\t\t− | \n\t\t\t− | \n\t\t\t100% | \n\t\t\t100% | \n\t\t\t[40] | \n\t\t|
5 | \n\t\t\tPer protocol susceptible population | \n\t\t\t96.6% | \n\t\t\t90.6% | \n\t\t\t100% | \n\t\t\t100% | \n\t\t\t[41] | \n\t\t
Cervarix® and Gardasil® efficacy
HPV = Human papillomavirus. According to protocol population = women HPV16 or HPV18 DNA negative during the vaccination schedule, that received 3 doses of the vaccine; Per protocol population = participants received 3 doses of vaccine or placebo within 12 months and were seronegative on PCR analysis for HPV6-, HPV-11, HPV-16, or HPV18 at day 1 through 1 month after the third dose.* Persistent infection correspond to infection detected for ≥6 months. N/A: not available.
Six major clinical trials enrolling around 44.000 females were conducted to evaluate the efficacy of Cervarix® (2 trials) and Gardasil® (4 trials). Most of the trials included subjects from the age of 15 to 26 years (except for Muñoz
Since cervical cancer is an unethical endpoint for the HPV prophylactic vaccines efficacy evaluation, the clinical trials concentrated on prevention of pre-cancerous high-grade cervical intraepithelial neoplasias (CIN 2 and 3). Results from these trials have shown the high efficacy of the prophylactic vaccines in preventing persistent infection and CIN 2/3 lesions and genital warts for Gardasil® and Cervarix®.
The double-blind randomized controlled PATRICIA (PApilloma TRIal against Cancer In young Adults) is the largest Cervarix® vaccine trial performed to date and it was conducted in more than 14 countries from Asia-Pacific, Europe, North America and Latin America. It included over 18.000 healthy women between 15 and 25 years of age with no more than six lifetime sexual partners; these women were enrolled irrespective of their HPV DNA status, HPV serostatus or cytology baseline.
Cervical cytologies and biopsies for 14 oncogenic HPV types were assessed by PCR. The primary endpoint for the vaccine efficacy was the development of CIN 2+ associated with HPV16 or HPV18 and as well non-vaccinated oncogenic HPV types (for cross-protection assessment) [34, 47].
Data from three different cohorts (ATP-E: according to protocol cohort for efficacy vaccinated: n=8093; control: n=8069; TVC: total vaccinated cohort = women receiving at least one dose of the Cervarix®: n=9319; control: n=9325; and TVC-naïve = no evidence of oncogenic HPV infection at baseline vaccinated: n=5822; control: n=5819) over a mean of 34.9 months was analyzed. The efficacy of the vaccine against CIN2/3 lesions associated with HPV16/18 was similarly high (around 98% for CIN2+ and 100% for CIN3+) in the ATP-E and TVC- naïve cohorts. For the TVC group the efficacy of the vaccine against CIN3+ lesions, irrespective of HPV DNA in lesions, was 30%.
The randomized, double-blind, placebo-controlled trials FUTURE I and FUTURE II included 18.174 women between 16-26 years of age from 24 different countries from Asia-Pacific, North America, Latin America and Europe. The primary endpoints for the Gardasil® efficacy clinical trial were a) incidence of genital warts, vulvar or vaginal intraepithelial neoplasia or cancer and b) the incidence of cervical intraepithelial neoplasia CIN2/3 and adenocarcinoma
For the FUTURE II study, which enrolled 12,167 women that were followed for an average of 3 years, Gardasil® efficacy for prevention of HPV-16/18 related CIN3 lesions was 97% in the per-protocol cohort (population negative for 14 HPV types and receiving all the three doses of the vaccine), 95% in the unrestricted susceptible population (population receiving one or more vaccination doses) and 45% in the intention-to-treat cohort (population with or without previous HPV infection). HPV16/11/16/18 related high grade vulvar and vaginal lesions could be prevented with 100% efficacy by vaccination with Gardasil® in the per-protocol, with 95% in the unrestricted susceptible, and with 73% in the intention-to-treat populations. Gardasil® efficacy for prevention of adenocarcinoma
After the data from several clinical trials ensuring safety, tolerability and efficacy of the HPV vaccines was published, discussions began about which vaccine should be implemented in public vaccination programs. To make this decision, the cost-effectiveness of the vaccines, potentially influenced by duration of protection, number of doses required for protection and the extend of cross-protection, needed to be evaluated.
It is a difficult and daunting task to directly compare results from the Cervarix® and Gardasil® clinical trials because of a) differences in the study population and cohorts for testing the vaccine efficacy, b) differences in the HPV typing and immunological assays and c) differences in the studies’ endpoints.
For this reason, an observer-blind study was designed to directly compare the immunogenicity and safety of both vaccines [23]. In this study, a total of 1106 women aged 18 to 45 years were enrolled and vaccinated either with Gardasil® or Cervarix®. One month after the third vaccination, sera from all the subjects were collected and the presence of neutralizing antibodies was measured by pseudovirions-based neutralization assay (PBNA). The PBNA showed that all women in both vaccine groups were HPV16 and HPV18 seropositive with the exception of two HPV18 seronegative subjects in the Gardasil® group.
The titers of anti-HPV16 and HPV18 neutralizing antibodies from serum and cervicovaginal secretions induced by Cervarix® were significantly higher than those induced by Gardasil® in all the tested age strata. The frequency of antigen-specific (HPV16 and HPV18) and memory B-cells were also higher in the Cervarix® than in the Gardasil® group [23].
Although both vaccines were licensed as 3-dose administrations over six months, this regime has been questioned and re-evaluated either for cost-effectiveness or for difficulties with administering all the doses within the stipulated time frame.
Recently a comparative analysis between the Costa Rica Vaccine Trial cohort was published where it was suggested that two and maybe even one dose of Cervarix® might be as effective against persistent HPV16 and HPV18 infections as the three doses [51]. What remains unclear is the duration of protection for the vaccination with fewer doses.
One surprising finding of the phase II and phase III clinical trials is that both vaccines induce cross-protection against non-vaccine HPV types.
A recent end-of-study analysis of the Cervarix® PATRICIA clinical trial, performed after 48 months of follow-up, evaluated the cross-protection against non-vaccine HPV types in persistent infection and high grade CIN2+ and CIN3+ lesions. In summary, this analysis reports consistent vaccine efficacy against HPV31, HPV33 and HPV45 for all the end-points [52]
The analysis of combined data from the Gardasil® FUTURE I and FUTURE II clinical trials reveals that vaccination reduced the rate of HPV-31/33/45/52/58 infection, CIN1-3 and AIS. However the reduction of HPV-31/33/45/52/58 related CIN2 lesions was not significant [53]
A meta-analysis study suggests that cross-protection efficacy against persistent HPV infection and CIN2 lesions is higher for Cervarix® than for Gardasil®. While Gardasil® can confer protection against the non-vaccine type HPV31, Cervarix® can efficiently protect against HPV 31, HPV 33 and HPV45. This study evaluated comparable populations in different clinical trials that used different methods to identify efficacy endpoints (e.g. genotyping of HPV to determine HPV persistent infection). The sensitivity of the methods used in clinical trials and population differences can influence the comparison between Cervarix® and Gardasil®[54].
A sub-analysis of an observer-blind study, performed to allow a direct comparison between Cervarix® and Gardasil®, evaluated cross-protection against non-vaccine HPV types for both vaccines. This study confirmed that both vaccines induce cross-reactive responses against HPV31 and HPV45 but that the responses were initially much lower for the Gardasil® vaccinated group. However, after 24 months the level of humoral responses for HPV31/45 was equally low for both vaccines. The only considerable difference between the vaccines shown in this study is the higher levels of T-cell response with the Cervarix® vaccine. Whether or not the T cell response is necessary for cross-protection remains unclear [55].
All the studies show lower levels of non-vaccine HPV antibody titers compared to the type-specific titers. One possibility to be considered is that the cross-protective responses will wane with time. There are on-going efforts in current phase IV surveillance studies addressing the degree and durability of cross-protective responses.
Vaccination with Gardasil® or Cervarix® does not lead to clearance of pre-existing HPV infections [56]. Considering the decreasing age of sexual debut in many countries, both vaccines target preadolescent girls and young women. The Advisory Committee on Immunization Practices (ACIP) recommend vaccination of females aged 13 to 26 years for Cervarix® and vaccination of 9 to 26 year old males and females for Gardasil® [57, 58].
Most of the clinical trials performed to evaluate efficacy of the prophylactic vaccines included subjects older than the primary target population. This is explained by a) the need of a population where the HPV infection happens at higher frequency for efficacy proof-of-principle purposes and b) legal and ethical limitations regarding the evaluation of sexual activity in the preadolescent population.
A Cervarix® clinical trial, performed in Denmark, Estonia, Finland, Greece, Netherlands and Russia, with an extension study (4 years follow-up) conducted in Denmark, Estonia and Finland, was designed to evaluate safety and immunogenicity of the bivalent vaccine in two age groups (10-14 and 15-25 years). According to the follow-up study, Cervarix® induced higher systemic and mucosal immune responses, which sustained for more than four years, in the 10-14 years group compared to the 15-25 years group [59].
Women can acquire HPV infections at any age. However, epidemiological data report that the highest prevalence of HPV infections occur in sexually active women under 25 years of age and decline with age progression [60].
Recent meta-analysis studies have been showing a second peak of HPV prevalence in women over 44 years [61]. There are several hypotheses explaining this phenomenon but the most plausible one is associated with changes in sexual behavior of women and their partners at this age.
Humoral responses to HPV vaccines are known to decrease gradually with age progression but the antibody levels remain several fold higher for years in vaccinated (46-55 years) than in non-vaccinated subjects who developed natural immunity in response to infection [62]. A recent analysis of the FUTURE I and FUTURE II clinical trials evaluated the efficacy of Gardasil® in HPV DNA positive women who were treated for cervical, vulvar, or vaginal disease. This study showed that vaccination with Gardasil® decreases by more than 40% the incidence of subsequent HPV-related diseases including genital warts and CIN1/2 lesions, irrespective of the HPV type in the lesion [63]. This finding suggests that including women older than 26 years in the vaccination program might prevent HPV persistent infection in naïve women and reduce re-infection for those that were already infected.
HPV infection of males is associated with genital warts, anogenital cancer, oral cancer, and recurrent respiratory papillomatosis. The overall incidence of HPV infection is very similar for men and women, although, in contrast to the situation in women, HPV infection in males does not seem to be age related [64].
Currently, Gardasil® is the only HPV prophylactic vaccine licensed for use in males. Their target population is boys and men aged 9 to 26 years. Its high immunogenicity, safety and efficacy against anogenital warts and perianal/perineal intraephitelial neoplasia in males has been reported in several clinical trial studies [65, 66].
Even though several mathematical models suggest that the inclusion of males in vaccination programs will not be a cost-effective strategy [67], the potential reduction of the health burden associated with HPV infection in males (e.g.: anal cancer and anogenital warts) and the possibility to reduce the risk of HPV transmission to women argue in favor of extending HPV vaccination programs to males.
One of the arguments against the vaccination of males is that immunization of females might already lead to enhanced herd immunity and thereby reduce male lesions as well. One factor not considered with this argument is the scenario of men who have sex with men (MSM) who cannot benefit from female vaccination.
The MSM population is one of the most affected by HPV warts and anal cancer. It is clear that this population will not benefit from female vaccination. Recently, a clinical trial enrolling 602 healthy men who have sex with men (16 to 26 years of age) showed efficacy and safety of Gardasil® against high-grade anal intraepithelial neoplasia (AIN2/3) [68].
Based on data of Gardasil® safety, efficacy against AIN2/3, estimates of disease and cancer resulting from HPV and cost-effectiveness, the Advisory Committee on Immunization Practices (ACIP) recommended routine use of the quadrivalent HPV vaccine in males aged 11 or 12 years [58].
Immunocompromised women and men are known to have higher incidences of HPV infection and HPV-related diseases including cervical and anal cancer. However, little is known about the efficacy and safety of the prophylactic vaccine in this population.
Few HPV clinical trials have been studying HIV positive populations. Among those, a clinical trial evaluated Gardasil® safety and immunogenicity in HIV infected children from 7 to 12 years, separated into three different groups according to their CD4+ T cells count. The vaccine was considered highly safe, with no CIN3 lesions being observed in the vaccinated group when compared to the control. Vaccination led to seroconversion of 99% of the immunized subjects; however, antibody titers for HPV16 and HPV18 were much lower (30-50%) than for the historical control (HIV uninfected children – Gardasil® vaccinated) [69], indicating a reduced response in this target population.
As levels of HPV16 and HPV18 antibodies were still comparable to HIV-uninfected women (16-26 years old) in whom the vaccine efficacy was confirmed, long-term studies with more subjects are necessary to determine vaccine efficacy in the HIV infected population.
As outlined above, the two commercial vaccines induce long lasting high titer, protective antibody responses against the HPV types included in the vaccines. The efficacy of preventing vaccine type induced lesions can reach up to 100%. This success is based on the exceptional immunogenicity of HPV virus-like-particles and the current vaccination programs will surely have significant impact on reducing the HPV associated cancer burden in the near future. Still, there are several shortcomings of the commercial vaccines which include costly production, need for invasive administration, low stability requiring intact cold chains in vaccine delivery and a narrow range of protection limited mainly to vaccine type papillomaviruses. Further, studies have shown that vaccination with the commercial vaccines has no impact on the progression of pre-existing lesions, i.e. neither Gardasil® nor Cervarix® seem to have a therapeutic effect [56]. Although basically all vaccines used in routine medicine are of prophylactic nature, this was not necessarily expected to be the case for the HPV VLP vaccines. In a number of preclinical studies it was demonstrated that vaccination of mice with VLPs induces strong cytotoxic T-cell responses against the L1 antigen and in case of L1-E7 chimeric particles also against the E7 portion [70-73]. The response had strong anti-tumorigenic properties in different tumor challenge models. Therefore, there was reason to hope for a vaccination benefit for humans already infected with the corresponding HPV type. Unfortunately, however, this benefit was not observed in clinical trials to date.
To overcome at least some of the limitations of the commercial vaccines a number of different approaches to develop a second generation PV vaccine are followed, some of which will be addressed in more detail below.
Both current commercial vaccines show excellent safety and efficacy profiles and there seems to be little room for improvement in either aspect when addressing the HPV type-specific protection. Some countries are considering or are in the process of implementing a two dose regiment, driven by the intention to minimize costs [74, 75]. Such deliberations would benefit from higher immunogenicity of the VLP vaccine, which could possibly be achieved by using stronger adjuvant systems. But naturally, it seems unlikely that Merck or GSK would find a sufficient economical motivation to move along this road. What’s more, there is only a limited repertoire of adjuvants that can be used in prophylaxis for a young target population.
Both Merck and GSK are probably not highly motivated in developing second generation HPV vaccines that would compete with their blockbusters. An exception is the nonavalent HPV VLP vaccine that is currently evaluated by Merck in clinical trials. A number of pre-clinical studies focused on the development of L1-based vaccines that overcome one or more of the limitations discussed above. These second generation approaches addressed delivery (e.g. oral), production systems (plant, E. coli), stability (e.g. capsomeres) and extension to therapeutic applications (chimeric L1 proteins) [76-78]. In light of the fact that the current VLP vaccines are inducing a limited degree of cross-protection, for which the nature is not yet known, one could envision modifying the L1 protein so as to extend the breadth of protection, but to our knowledge, this strategy is currently not pursued.
As indicated above, the protective range of Cervarix® and Gardasil® is mainly limited to the vaccine type papillomaviruses. In their clinical trial GSK could show that immunization with Cervarix® induces cross-protection against additional types such as HPV 31, 33 and 45 and Gardasil® induces protection against HPV 31, albeit at lower efficacy. As a consequence, in 2010 the European Medicines Agency has approved the amendment of the license of Cervarix® in prevention of HPV 31, 33 and 45 induced lesions. The molecular mechanisms for the enhanced cross-protection of Cervarix® in comparison to Gardasil® is not fully understood. One explanation could be the fact that Cervarix® is inducing higher titers against HPV 16 and HPV 18, possibly due to the stronger adjuvants used in the formulation of Cervarix®. Another explanation could be structural differences of the VLPs contained in the two vaccines.
However, despite this extended cross-protection observed for Cervarix® about 20% of cervical cancer cases remain uncovered by the vaccine. To breach this gap, Merck MSD is currently evaluating a nonavalent HPV VLP vaccine in phase III clinical trials. In addition to the non-oncogenic HPVs 6 and 11, this vaccine includes VLPs of HPV types 16, 18, 31, 33, 45, 52 and 58 and theoretically would reach close to 88% efficacy. It remains to be determined whether this cocktail of nine different VLPs is able to induce prolonged protective responses against the corresponding HPV types or if due to interference this may not be possible. Further, because of increasing vaccine complexity this strategy will be limited due to rising costs in production. Also, it will be difficult to prove vaccine efficacy in preventing cervical dysplasia induced by rather rare HPV types, such as HPV 52 and HPV 58, if neutralizing antibodies or at least prevention of infection by these types are not accepted as surrogate markers by the licensing agencies.
There are more than 200 different papillomaviruses infecting vertebrates. Among them are roughly 50 types for which there is a theoretical interest of implementing prophylaxis and these include oncogenic HPVs, skin type HPVs relevant in immune compromised patients, bovine PV infecting cattle [79] and horses and PV viruses infecting pets. It has been shown in a number of studies that genetic vaccination with codon-adapted L1 genes leads to the induction of high titer neutralizing antibodies. Vaccination has been performed by intramuscular needle injection or by the use of a gene gun [80-90]. We observed particularly strong neutralizing antibody responses when administering codon-modified L1 genes using a tattooing device [84, 91]. In addition to delivering the expression constructs to muscle and/or antigen presenting cells, tattooing induces a certain degree of local tissue damage which might serve as a danger signal [92, 93]. The great advantage of immunization with naked DNA is the ease of constructing and producing the vaccine vectors for many different L1 antigens since standardized procedures can be applied. Also, DNA is a very stable molecule making the need for intact cold chains in vaccine distribution obsolete. In addition, it has been shown that cocktails of different L1 expression constructs can be applied to mount a broad range of protection, although some kind of interference between different L1s has been observed [94].
Currently, no clinical testing involving human subjects is being performed with naked DNA or with a genetic vector. For one, DNA immunization has not found its way to human immune prophylaxis to date. The main reason is the much lower efficacy of DNA vaccines in primates compared to the murine system. Further, there are concerns about the safety of DNA vaccines in general. Although these concerns are of theoretical nature only, they still pose a major hurdle for application in routine vaccine prophylaxis.
The ease of targeting multiple L1 antigens has also been a motivation to evaluate viral vector based genetic approaches. Different viral vectors have been used and these include vaccinia virus, vesicular stomatitis virus, and adenoviruses [95-98]. High titer neutralizing antibody responses were induced in vaccinated mice. Additionally, in some of the studies strong cellular immune responses against the L1 antigen could be demonstrated. Using the cottontail rabbit papillomavirus model, it was shown that single intranasal administration of recombinant vaccinia virus [99] or vesicular stomatitis virus (VSV) [96, 97] induces anti-L1 antibodies and protections against CRPV challenge, although the latter could also have been a consequence of the induction of cellular immune responses against L1.
Berg et al. ([98] [100] produced correctly folded canine oral PV VLPs using recombinant adenoviruses. Immunization of mice led to high titer neutralizing antibody responses, but the recombinant adenoviruses have not yet been tested in the COPV challenge model.
When considering administration, the use of complex virus systems including vaccinia virus, VSV and adenoviruses faces significant safety issues. Moreover, most vaccinations will likely be limited to single administration due to the strong responses against the vectors. In this light it might not be possible to generate responses against L1 proteins of multiple PVs.
Adeno-associated virus (AAV) vectors combine the simplicity of naked DNA with the efficacy of viral vector gene delivery. AAV vectors are extremely stable and can be lyophilized without compromising their transduction activity. Also, these viral vectors do not encode for viral gene products. We have used AAV vectors for intranasal and systemic delivery of the L1 gene. Single doses of AAV-L1 induce long lasting (>1 yr) neutralizing antibody responses in mice. The intranasal application also induced mucosal antibodies and cellular immunity. Non-adjuvanted intranasal application in macaques with recombinant AAV9 vectors also induced immunity against the encoded L1 antigen [101-104]. Liu and colleagues reported on the co-administration of AAV-L1 vectors together with a recombinant adenoviruses encoding for granulocyte macrophage colony-stimulating factor [105]
In addition to viral vectors, L1 has also been delivered by live prokaryotic vectors such as Salmonella enterica Typhii [106-109] and recombinant Bacille Calmette-Guerin (rBCG) [110, 111]. Nardelli-Haefliger was the first to demonstrate that live L1-recombinant bacteria (S. typhii) induced mucosal and systemic antibody responses in mice. In another study, Govan et al. showed that rabbits vaccinated with rBCG encoding the CRPV L1 protein are protected against viral challenge [110]. This protection might, however, in part be due to cellular immune responses against the L1 antigen, although the authors could demonstrate
The current HPV vaccines are produced either in yeast (Gardasil®) or insect cells infected with recombinant baculoviruses (Cervarix®). It is not disclosed by the vaccine manufacturers what the production costs per dose really are, but insect cells present a rather complex platform and yeast cells provide challenges in the extraction procedures. In the early phases of HPV VLP technology, several labs worked on expressing L1 in
In a number of studies the production of L1 antigens in transgenic plants has been evaluated. Earlier studies showed that the surface antigen of hepatitis B virus can be expressed and assembled in transgenic plants [116]. Importantly, oral delivery of unprocessed plant material induced HBsAg specific immunity in mice and healthy volunteers [117]. This report ignited the idea that vaccine antigens can be produced with the aid of transgenic plant technology. The great advantage of plants is the simplicity by which vast quantities of biomass can be produced with all required technology already in place. Bypassing the requirement for antigen extraction and purification would allow to meet the worlds growing, yet unmet, demand for cheap vaccines. In this light, production of L1 in plants was initiated, [118-128], and immunogenicity after either oral or systemic delivery was confirmed. Yield of L1, which initially posed a major problem, improved significantly to more than 10% of the total soluble protein [125].
Today’s consensus on antigen production in plants stresses standardized extraction and purification to ensure antigens with defined properties and limited inter-batch variability will be an essential criteria. Also, much of the L1 antigen in the plant tissue is incorrectly folded and hence has only little immunogenicity. Overall, there are strong resentments by regulatory agencies and vaccine manufacturers on introducing poorly standardize-able oral vaccines originating from partially processed plant material.
In summary, there are tremendous hurdles that novel second generation vaccines based on the L1 antigen must be overcome starting with facing and competing with the two existing commercial vaccines. The main challenge seems to be the need for demonstrating non-inferiority. Licensing of Gardasil® and Cervarix® has been a mammoth task, involving tens of thousands participants in clinical trials. It is very unlikely that such evaluation can be reproduced with a vaccine approach that presents only an incremental improvement in one of the other shortcomings of Gardasil® and Cervarix®. Other equally important issues are safety and simplicity of second generation vaccines, especially in light of the target population’s young age. Lastly, intellectual property is an important factor in vaccine development. While the tight patent situation on L1 VLP technology might eventually be less stringent in the coming years, this will also leave novel developments without sufficient protection, making major investments for manufacturers less attractive.
At the time when PV VLP technology started to have its major impact on papillomavirus research and vaccine development, the group of Saveria Campo in Glasgow reported that vaccination of cattle with a bacterially produced minor capsid protein L2 induced protection against challenge with infectious BPV 4 virus [129]. The authors identified epitopes located in a region of L2 encompassing amino acids 131-151 of BPV 4. Although the report describes these epitopes as B-cell epitopes, no neutralization assay could be performed at the time and hence an involvement of cellular immunity could not be ruled out. Also, the antigens that were either GST-L2 fusion proteins or conjugated peptides were of rather poor immunogenicity. As the field was moving towards VLP vaccines that induce very strong protective effects, L2 was not given further thought as a vaccine antigen at the time.
Later, Richard Roden and his colleagues investigated in detail the suitability of L2 as a vaccine antigen. They observed that L2 antigens purified from
The presence of neutralizing and cross-neutralizing epitopes in the N-terminus of L2 was reported and confirmed by others. Kondo and colleagues mapped several regions in the L2 protein between amino acids 1-140 [135]. Some of the neutralizing epitopes were later confirmed by others, however it seems clear today that only one epitope, comprising amino acids 17-36, consistently elicited cross-protection [136-138].
After identifying the target region in the L2 protein, the major challenge in developing L2 as a vaccine antigen was posed by L2’s low immunogenicity compared to L1. No or very little neutralizing activity is induced when fragments or peptides of L2 are used as antigens [129, 137]. Further, VLPs composed of L1 and L2 do not induce measurable anti-L2 responses. Because of this, a number of strategies were pursued with the goal of increasing immunogenicity of the L2 cross-neutralizing epitope.
Alphs et al. observed a strong increase in immunogenicity of the 17-36 epitope when conjugating the L2 peptide to a synthetic lipopeptide (TLR2 agonist) and a broadly acting T-helper epitope [139]. This antigen induced rather high neutralizing titers against HPV 16 while responses against other high-risk HPVs including HPV 18 or HPV 45 were 1-2 orders of magnitude lower. Still, this fully synthetic L2 vaccine provided an elegant basis for the development of a L2 vaccine. Jagu et al. reported that a concatenated L2 fusion protein, consisting of the amino acids 11-88 of five different HPV types induced strong neutralization and cross-neutralization and was superior compared to monotypic HPV 16 L2 antigen. This approach is expected to enter a clinical phase in 2013.
Displaying the 17-36 epitope on bacteriophage PP7 capsids was shown to be an attractive alternative approach in generating a functional L2-based vaccine [140, 141]. VLPs of bacteriophage PP7 can be produced in large quantities and are tolerant for the insertion of heterologous peptides. Immunization of mice leads to high titers of ELISA reactive L2-specific antibodies. Cross-protective neutralization of HPV pseudovirions was shown in an
A ‘natural’ scaffold for the presentation of L2 epitopes would be to insert the cross-neutralizing epitope into L1 loops located on the VLP surface. This would provide for a highly repetitive presentation of the L2 region. Schellenbacher et al. pursued this approach and tested various peptide insertions into the BPV1 and HPV 16 L1 protein [142]. Such insertions often interfere with proper assembly of the L1 into higher ordered structures but the authors were able to produce and purify a number of L1-L2 chimeric particles. They demonstrated that the CVLPs still induced L1-specific neutralization, indicating mostly correct conformation of the L1 protein. More importantly, chimeric particles carrying the 17-36 epitope of HPV 16 L2 induced neutralizing antibody responses in rabbits against HPV 5, 11, 16, 18, 45, 52, 58 pseudovirions with titers ranging from 1:100 to 1:10,000.
Recently, we have developed a strategy to boost the immunogenicity of the L2 cross-neutralizing epitope by using bacterial thioredoxin (
Ultimately, there is convincing evidence that the L2 protein of HPV contains a number of neutralizing epitopes and importantly one major cross-neutralizing epitope. It is also clear that due to the low immunogenicity of L2 an appropriate scaffold and/or adjuvant system is required. Still, there are several issues to be addressed. First, no systematic comparison of the different strategies of L2 epitope presentation has been carried out. No consensus has been reached as to which parameters for L2 vaccination would be an indicator for vaccine efficacy or would present a correlate for protection
A nowadays routine assay is the pseudovirion-based neutralization (PBNA) assay developed by Buck et al. that measures transduction efficiency of PV capsids encapsidating a reporter gene. In the presence of L1 or L2 neutralizing antibodies or compounds that interfere with virus infection such as carrageenan (see below), transduction of cells is inhibited. This assay is considered the gold-standard for
As described above, early vaccination experiments have been carried out in rabbits and cows, followed by challenge with the corresponding virus, CRPV or BPV. Readout was induction of papillomas. The CPRV model was extended for the use of HPV by ‘pseudotyping’, i.e. encapsidating CRPV genomes into HPV 16 capsids. By this, rabbits can serve as an
However, despite the highly valuable contribution of BPV, CRPV, and COPV models, only a few laboratories around the world had the available means and resources to establish them for routine use.
The laboratory of John Schiller developed a mouse model for PV infection that can find widespread routine application more easily [13]. In this model, the genital mucosa is infected with pseudovirions encapsidating a luciferase reporter gene. Infection can be quantified by
Interestingly and similar to the L2-PBNA, the
The existing animal models are unlikely to make functional
Concerns about the limitation of the HPV vaccines (e.g.: type specificity and costs) stimulate constant research on alternative strategies for HPV prevention.
Condoms, spermicides, microbicides, circumcision and contraceptives are included in the extensive list of preventive measures that have been shown to curb HPV infection and persistence.
Condoms are known to be protective against many sexual transmitted diseases such as HIV, gonorrhea, chlamydia and tricomoniasis. However, a cross- sectional analysis conducted in men (18-70 years old) from Brazil, Mexico and United States, showed that HPV infection can be reduced but not completely prevented by the use of condoms. Several factors can be attributed to the low efficacy of condoms in preventing HPV infection, including inappropriate usage leading to condom breakage and slippage and the fact that condoms cannot cover all the HPV infected genital areas [146].
Circumcision has been reported to play a role in preventing sexual transmission of HIV, herpes simplex and HPV [147-149]. A recent trial reported that circumcised males have a reduced prevalence of oncogenic HPV types by 32% to 35% and that this effect might be transferred to the partners of circumcised men [150]. Even though the positive effect of the circumcision against HPV persistence has been confirmed by several studies [151-153], ethical issues and complications make circumcision a procedure that most likely will not be routinely adopted.
Different microbicides have been studied for their properties to protect against sexual transmitted infections (STIs). Among those, the spermicide the nonoxynol-9 (N-9) was the most promising. This spermicide, largely available in the market during the 90s, has shown to be protective
Carrageenan is a sulfated polysaccharide compound routinely used as thickening ingredient in food products as well as in sexual lubricants and therefore has an excellent safety record. It is derived from seaweed and studies have shown that it confers protection against HIV and HPV
Recently, a dendrimeric gel microbicide (VivaGel – SPL7013) was developed by Starpharma for prevention of infections by HIV and HSV-2. The efficacy and safety of the gel have been demonstrated
We would like to acknowledge Frank Burkart for designing figures.
In the recent years, machine learning techniques have been utilized to solve problems at hand across multitudes of industries and topics. In the healthcare industry, these techniques are often applied to a variety of healthcare claims and electronic health records data to garner valuable insights into diagnostic and treatment pathways in order to help optimize patient healthcare access and treatment process [1]. Unfortunately, many of these applications resulted in inaccurate or irrelevant research results, as proper research protocols were not fully followed [2]. On the other hand, statistics has been the basis of analysis in healthcare research for decades, especially, in the areas of clinical trials and health economics and outcomes research (HEOR), where the precision and accuracy of analyses have been the primary objectives [3]. Furthermore, the classical statistics methodologies are often preferred in those research areas to ensure the ability to replicate and defend the results and ultimately, the ability to publish the research content in peer-reviewed medical journals [3]. The increased availability of data, including data from wearables, provided the opportunity to apply a variety of analytical techniques and methodologies to identify patterns, often hidden, that could help with optimization of healthcare access as well as diagnostic and treatment process [4].
With the rapid increase in data from the healthcare and many other industries, it is important to consider how to select well - suited statistical and machine learning methodologies that would be best for the problem at hand, the available data type, and the overall research objectives [5]. Machine learning alone or complemented by statistical modeling is becoming, not just a more common, but a desired convergence to take advantage of the best of both approaches for advancing healthcare outcomes [1]. Please note that this book chapter was originally posted on the Cornell University’s research working article website: https://arxiv.org. The book chapter content is mostly the same between the two versions [6].
Machine learning (ML) is considered a branch of artificial intelligence and computer science that focuses on mimicking human behaviors through a set of algorithms and methods that use historical values to predict new values [7], without specifically being coded to do so and thereby learning over time [8, 9]. ML is grounded in statistical learning theory (SLT), which provides the constructs used to create prediction functions from data. One of the first examples of SLT was the creation of the support vector machine (SVM), the supervised learning method that can be used as for both classification and regression and has become a standard in modeling how to recognize visual objects [7]. SLT formalizes the model that makes a prediction based on observations (i.e., data) and ML automates the modeling [7].
SLT sets the mathematical and theoretical framework for ML as well as the properties of learning algorithms [7] with the goals of providing mechanisms for studying inference and creating algorithms that become more precise and improved over time [8]. SLT is based multivariate statistics and functional analysis [8]. Functional analysis is the branch of statistics that measures shapes, curves, and surfaces, extending multivariate vector statistics to continuous functions and finding functions that describe data patterns [8]. Inductive inference is the process of generalizing and modeling past observations to make predictions for the future; SLT formalizes the modeling concepts of inductive inference, while ML automates them [8].
For example, pattern recognition is considered a problem of inductive inference and SLT, as it is a curving-fitting problem, and one of the most common applications of ML [7, 8, 9]. Pattern recognition is not suited for traditional computer programming as the inferences needed are not free of assumptions and the patterns are not easily described or labeled programmatically with deterministic functions. The standard mathematics behind SLT makes no assumptions on distributions, uses stochastic functions that can include humans labeling the “right” classification, i.e., training data, and can assume that the probability of the occurrence of one observation is independent of another thereby including the concept of randomness [7, 8, 9]. These tenets are therefore those of ML as well.
SLT also provides the definition of terms often using in ML such as overfitting, underfitting and generalization. Overfitting is when the presence of noise in the data negatively affects training and the ultimate model performance because the noise is being incorporated into the learning process, thereby giving error when the model sees new data [8, 9]. Underfitting is when the noise impacts both performance on training data as well as new and unseen data [9]. In ML, discussion about underfitting and overfitting are often used to describe models that do not generalize the data effectively and might not present the right set of data elements to explain the data patterns and posited hypotheses [9]. Underfitting is often defined when model which is missing features that would be present in the most optimized model, akin to a regression model not fully explaining all of the variance of the dependent variable [9]. In a similar vein, overfitting is when the model contains more features or different features than is optimal, like a regression model with autocorrelation or multicollinearity [9].
The general goal of learning algorithms and therefore ML model optimization is to reduce the dimensions, features, or data variables to the fewest number needed as that reduces noise or the impact of trivial variables that can overfit or unfit [8, 9]. A regularization model can then become generalized to perform not just on the past or the training data, but also on future and yet unseen data [8, 9]. Although true generalization needs both the right modeling criteria as well as strong subject matter knowledge [8].
Often dimension reduction approaches like Principal Component Analysis (PCA) or boot strapping techniques used along with subject matter expertise can help resolve how to refine models, combat fit challenges, as well as improve generalization potential [9, 10]. Furthermore, understanding the studied population and data characteristics can further help define the data to be used, variable selection, and proper model set up [10].
Statistical modeling is based on SLT and use of mathematical models and statistical assumptions to generate sample data and make predictions about the real world occurrences. A statistical model is often represented as a collection of probability distributions on a set of all possible outcomes. Furthermore, statistical modeling has evolved in the last few decades and shaped the future of business analytics and data science, including the current use and applications of ML algorithms. On the other hand, machine learning does not require many assumptions and interventions when running algorithms in order to accurately predict studied outcomes [7].
There are similarities between ML and statistical modeling that are prevalent across most analytical efforts. Both techniques use historical data as input to predict new output values, but they vary as noted above on the underlying assumptions and the level of analyst intervention and data preparation.
Overall, machine learning foundations are based from statistical learning theory, and it is recommended for the data scientists to apply SLT’s guiding rules during analysis. While it may seem as a statistical background and understanding is not required when analyzing the underlying data, this misconception often leads to data scientist’s inability to set up proper research hypothesis and analysis due to a lack of understanding of the problem and the underlying data assumptions as well as caveats. This issue can in turn result in biased and irrelevant results as well as unfounded conclusions and insights. With that in mind, it is important to evaluate the problem at hand, and consider both statistical modeling and ML as possible methods to be applied. Understanding the underlying assumptions of the data and statistical inference can help support proper technique selection and guide the pathway to solution [11]. In the later sections of the chapter, application of both techniques will be provided and the reasoning for selecting the methods presented to guide future research.
As mentioned above, the similarities between ML and statistical modeling start with the underlying assumption that data or observations from the past can be used to predict the future [7]. The variables included in the analysis generally represent two types: dependent variables, that in ML are called targets, and independent variables, that in ML are called features. The definition of the variables is the same across both techniques [8]. Furthermore, both ML and statistical modeling leverage the available data in a way that allow for generalization of results to larger population [7]. The loss and risk associated with the models accuracy and representation of the real world occurrence is described frequently in terms of mean squared error (MSE). In statistical modeling, MSE is the difference between the predicted value and the actual value and is used to measure loss of the performance of predictions. In the ML, the same MSE concept is presented via a confusion matrix that evaluates a classification problem\'s accuracy [9].
Differences between machine learning and statistical modeling are distinct and based on purposes and needs for the analysis as well as the outcomes. Assumptions and purposes for the analysis and approach can vastly differ. For example, statistics typically assumes that predictors or features are known and additive, models are parametric, and testing of hypotheses and uncertainty are forefront. On the other hand, ML does not make these assumptions [12]. In ML, many models are based on non-parametric approaches where the structure of model is not specified or unknown, additivity is not expected, and assumptions about normal distributions, linearity or residuals, for example, are not needed for modeling [10].
The purpose of ML is predictive performance using general purpose learning algorithms to find patterns that are less known, unrelated, and in complex data without a priori view of underlying structures [10]. Whereas in statistical modeling, consideration for inferences, correlations, and the effects of a small number of variables are drivers [12].
Due to the differences in the methods’ characteristics, it is important to understand the variations in application of the techniques when solving healthcare problems. For example, one typical application of statistics is to analyze whether a population has a particular medical condition. For some diseases such as diabetes, the condition is easily screened for and diagnosed using distinct lab values, such as elevated and increasing HbA1C over time, high glucose levels and low insulin levels, often due to insulin depletion occurring from unmanaged diabetes. Also conditions such as hypertension can easily be detected at home or in the healthcare provider’s office using simple blood pressure measurement and monitoring, and wearables can identify when patients are experiencing atrial fibrillation, abnormal heart rhythms and even increased patient falls (possible syncope). Therefore, analyses of patients with these easily measurable conditions can be done simply by qualifying patients based on lab values or biomarkers falling within or outside of certain ranges. One of the simplest examples is identifying patients with diabetes [13]. This can be accomplished by using A1C levels to group patients as having no diabetes (A1C < 5.7), pre-diabetes (AIC of 5.7–6.4), or diabetes (A1C > 6.4). These ranges are based on American Diabetes Association Diagnosis Guidelines and a very high, medically accepted correlation between AIC levels and the diagnosis of diabetes [14].
On the other hand, if the objective of the research is to predict which pre-diabetic patients are most likely to progress to diabetes, a myriad of factors influence diabetes progression including extent of chronic kidney disease, high blood pressure, insulin levels over time, body mass index/obesity, age, years with diabetes, success of prior therapy, number and types of prior therapies, family history, coronary artery disease, prior cardiovascular events, infections, etc. A complicated combination of comorbidities, risk factors, and patient behavior can lead to differing diabetes complications and varying outcomes makes prediction more challenging and thus it represents a good candidate for the use of machine learning techniques. Classification models such as gradient boosting tree algorithms have been used to successfully predict diabetes progression, especially earlier in the disease. While there any many diabetes risk factors and co-morbidities, these disease characteristics are well studied over many years, thus enabling stable predictive models which perform well over time [14].
Overall, machine learning is highly effective when the model uses more than a handful of independent variables/features [10]. ML is required when the number of features (p) is larger than the number of records or observations (n) – this is called the curse of dimensionality [15, 16], which increases the risk of overfitting, but can be overcome with dimensionality reductive techniques (i.e., PCA), as part of modeling [15] and clinical/expert input on the importance or lack thereof of certain features, is it relates to the disease or its treatment. Additionally, statistical learning theory teaches that learning algorithms increase their ability to translate complex structures from data at a greater and faster rate than the increase of sample size capture can alone provide [8]. Therefore, statistical learning theory and ML offer methods for addressing high-dimensional data or big data (high velocity, volume and variety) and smaller sample sizes [17], such as recursive feature elimination and support vector machines, boosting, or cross validation which can also minimize prediction error [18].
In the healthcare industry, machine learning models are frequently used in cancer prediction, generally in three areas: (1) predicting a patient with a cancer prognosis/diagnosis, (2) predicting cancer progression, and (3) predicting cancer mortality. Of these, predicting whether a patient may have a cancer prognosis/diagnosis can be more or less difficult depending on the tumor type. Certain cancers such as lung cancer, breast cancer, prostate cancer, and skin cancer are evaluated based on specific signs and symptoms, and non-invasive imaging or blood tests. These cancers are easier to predict. Conversely, cancers with non-descript symptoms such fatigue, dizziness, GI pain and distress, and lack of appetite are much more difficult to predict even with machine learning models as these symptoms are associated with multiple tumor types (for example esophageal, stomach, bladder, liver, and pancreatic cancer) and also mimic numerous other conditions [14].
For cancers with vague symptoms, understanding the patient journey is very important to cancer prediction. If a prediction period is too long and does not reflect the time period before diagnosis when symptoms develop, the model may overfit due to spurious variables not related to the condition. If the prediction period is too short, key risk factors from the patient record could be missing. Variable pruning is required in these situations. A multi-disciplinary team including business and clinical experts can help trim unrelated variables and improve model performance [14].
Model validation is an inherent part of the ML process where the data is split into training data and test data, with the larger portion of data used to train the model to learn outputs based on known inputs. This process allows for rapid structure knowledge for primary focus on building the ability to predict future outcomes [15]. Beyond initial validation of the model within the test data set, the model should be further tested in the real world using a large, representative, and more recent sample of data [19]. This can be accomplished by using the model to score the eligible population and using a look forward period to assess incidence or prevalence of the desired outcome. If the model is performing well, probability scores should be directly correlated to incidence/prevalence (the higher the probability score, the higher the incidence/prevalence). Model accuracy, precision, and recall can also be assessed using this approach [20].
Epidemiology studies and prior published machine learning research in related areas of healthcare can help benchmark the performance of the model relative to the baseline prevalent or incident population for the condition to be predicted. Machine learning models created using a few hundred or thousand patients often do not perform as well in the real world. Careful variable pruning, cohort refinement and adjustment of modeling periods can often resolve model performance problems. Newer software can be used to more quickly build, test, and iterate models, allowing users to easily transform and combine features as well as run many models simultaneously and visualize model performance, diagnosis and solve model issues [21].
Machine learning algorithms are a preferred choice of technique vs. a statistical modeling approach under specific circumstances, data configurations, and outcomes needed.
As noted above, machine learning algorithms are leveraged for prediction of the outcome rather than present the inferential and causal relationship between the outcome and independent variables/data elements [17, 22]. Once a model has been created, statistical analysis can sometime elucidate and validate the importance and relationship between independent and dependent variables.
Machine Learning algorithms are learner algorithms and learn on large amount of data often presented by a large number of data elements, but not necessarily with many observations [23]. Ability of multiple replications of samples, cross validation or application of boot strapping techniques for machine learning allows for wide datasets with many data elements and few observations, which is extremely helpful in predicting rare disease onset [24] as long as the process is accompanied with real world testing to ensure the models are not suffering from overfitting [18, 19]. With the advent of less expensive and more powerful computing power and storage, multialgorithm, ensembled models using larger cohorts can be more efficiently built. Larger modeling samples that are more representative of the overall population can help reduce the likelihood of overfitting or underfitting [25]. A large cohort imposes various issues and of priority is the ability to identify the set of independent variables that are most meaningful and impactful. These significant independent variables provide a predictive and/or inferential model that can be readily acceptable in providing a real-world application. The variables in such instances may also result into more realistic magnitude and direction of the causal relationship between the independent and outcomes variables of interest.
A recent example for a real-world example in healthcare for machine learning algorithm application is to identify the likelihood of hospitalization for high-risk patients diagnosed with Covid 19. The dataset leveraged included over 20,000 independent variables across healthcare claims data for diagnostics and treatment variables. The best optimal ML model consisted of approximately 200 important predictors variables such as age, diagnosis like Type 2 diabetes/CKD/Hypertension, frequency of office visits, Obesity amongst others. None of the variables in this example were ‘new’, however, the magnitude and direction as a result of the ML exercise may illustrate the ‘true’ impact of each independent variable, a feature that is a serious limitation in traditional statistical modeling [26].
Furthermore, as explained above, statistical models tend to not operate well on very large datasets and often require manageable datasets with a fewer number of pre-defined attributes/data elements for analysis [23]. The recommended number of attributes is up to 12 in a statistical model, because these techniques are highly prone to overfitting [25]. This limitation creates a challenge when analyzing large healthcare datasets and require application of dimension reduction techniques or expert guidance in allowing to eliminate the number of independent variables in the study [23].
In machine learning algorithms, there are fewer assumptions that need to be made on the dataset and the data elements [5]. However, a good model is usually preceded by profiling of the target and control groups and some knowledge of the domain. Understanding relationships within the data improve outcomes and interpretability [27].
Machine learning algorithms are comparatively more flexible than statistical models, as they do not require making assumptions regarding collinearity, normal distribution of residuals, etc. [5]. Thus, they have a high tolerance for uncertainty in variable performance (e.g., confidence intervals, hypothesis tests [28]. In statistical modeling emphasis is put in uncertainty estimates, furthermore, a variety of assumptions have to be satisfied before the outcome from a statistical model can be trusted and applied [28]. As a result, the statistical models have a low uncertainty tolerance [25].
Machine learning algorithms tend to be preferred over statistical modeling when the outcome to be predicted does not have a strong component of randomness, e.g., in visual pattern recognition an object must be an E or not an E [5], and when the learning algorithm can be trained on an unlimited number of exact replications [29].
ML is also appropriate when the overall prediction is the goal, with less visibility to describe the impact of any one independent variable or the relationships between variables [30], and when estimating uncertainty in forecasts or in effects of selected predictors is not a requirement [28]. However, often data scientists and data analysts leverage regression analytics to understand the estimated impact, including directionality of the relationships between the outcome and data elements, to help with model interpretation, relevance, and validity for the studied [27]. ML is also preferred when the dataset is wide and very large [23] with underlying variables are not fully known and previously described [5].
Machine learning requires no prior assumptions about the underlying relationships between the data elements. It is generally applied to high dimensional data sets and does not require many observations to create a working model [5]. However, understanding the underlying data will support building representative modeling cohorts, deriving features relevant for the disease state and population of interest, as well as understanding how to interpret modeling results [19, 27].
In contrast, statistical model requires a deeper understanding how the data was collected, statistical properties of the estimator (p-value, unbiased estimators), the underlying distribution of the population, etc. [17]. Statistical modeling techniques are usually applied to low dimensional data sets [25].
Robert Tibshirani, a statistician and machine learning expert at Stanford University, calls machine learning “glorified statistics,” which presents the dependence of machine learning techniques on statistics in a successful execution that not only allows for a high level of prediction, but interpretation of the results to ensure validity and applicability of the results in the healthcare [17]. Understanding the association and knowing their differences enables data scientists and statisticians to expand their knowledge and apply variety of methods outside their domain of expertise. This is the notion of “data science,” which aims to bridge the gap between the areas as well as bring other important to consider aspects of research [5]. Data science is evolving beyond statistics or more simple ML approaches to incorporate self-learning and autonomy with the ability to interpret context, assess and fill in data gaps, and make modeling adjustment over time [31]. While these modeling approaches are not perfect and more difficult to interpret, they provide exciting new options for difficult to solve problems, especially where the underlying data or environment is rapidly changing [27].
Collaboration and communication between not only data scientists and statisticians but also medical and clinical experts, public policy creators, epidemiologists, etc. allows for designing successful research studies that not only provide predictions and insights on relationships between the vast amount of data elements and health outcomes [30], but also allow for valid, interpretable and relevant results that can be applied with confidence to the project objectives and future deployment in the real [30, 32].
Finally, it is important to remember that machine learning foundations are based in statistical theory and learning. It may seem machine learning can be done without a sound statistical background, but this leads to not really understanding the different nuances in the data and presented results [17]. Well written machine learning code does not negate the need for an in-depth understanding of the problem, assumptions, and the importance of interpretation and validation [29].
As mentioned earlier in the chapter, machine learning algorithms can be leveraged in the healthcare industry to help evaluate a continuum of access, diagnostic and treatment outcomes, including prediction of patient diagnoses, treatment, adverse events, side effects, and improved quality of life as well as lower mortality rates [24].
As shown in Figure 1, often these algorithms can be helpful in predicting a variety of disease conditions and shortening the time from awareness to diagnosis and treatment, especially in rare and underdiagnosed conditions, estimate the ‘true’ market size, predicting disease progression such as identifying fast vs. slow progressing patients as well as determinants of suitable next line change [32]. Finally, the models can be leveraged for patient and physician segmentation and clustering to identify appropriate targets for in-person and non-personal promotion [30].
Examples of Machine Learning Applications in Healthcare Analytics [
There are, however, instances in which machine learning might not be the right tool to leverage, including when the condition or the underlying condition have a few known variables, when the market is mature and has known predetermined diagnostic and treatment algorithm, and when understanding correlations and inference is more important than making prediction [5].
One aspect of the machine learning process is to involve a cross functional team of experts in the healthcare area to ensure that the questions and problem statement along with hypothesis are properly set up [33, 34]. Many therapeutic areas require in-depth understanding of the clinical and medical concepts (i.e., diagnostic process, treatment regimens, potential adverse effects, etc.), which can help with the research design and selection of the proper analytical techniques. If the expert knowledge is not considered or properly captured in the research design, it might lead to irrelevant, invalid, and biased results, and ultimately invalidate the entire research study [33, 34].
Using a real example of a project with the goal of predicting the risk of hypertension due to underlying comorbid conditions or induced by medication, the decision to lead with machine learning vs. statistical modeling can be based on explicit criteria that can be weighed and ranked based on the desired outcome of the work [17, 32]. Please see Figure 2 presenting an example of the approach.
Criteria for Choosing the Predominant Approach for a Project.
As shown in Figure 2, pending the research objectives, machine learning or statistical modeling or both techniques could be the right method(s) to apply. For example, shifts in market trends, including shifts in patient volume of diagnosis and treatment present a suitable example when a statistical modeling type of analysis should be utilized. On the other hand, trying to predict patients with a high risk for hypertension requires the utilization of ML approaches. Leveraging both methods is best suited when predictive power and explanatory reasoning is needed to understand the important factors driving the outcome and their relative magnitudes and inferences.
Machine learning requires fewer assumptions about the underlying relationships between the data elements. It is generally applied to high dimensional data sets and require fewer observations to create a working model [5]. In contrast, statistical model requires an understanding of how the data was collected, statistical properties of the estimator (p-value, unbiased estimators), the underlying distribution of the population, etc. [17]. Statistical modeling techniques are usually applied to low dimensional data sets [25]. Statistical modeling and ML are not at odds but rather complementary approaches that offer choice of techniques based on need and desired outcomes. Data scientists and analysts should not necessarily have to choose between either machine learning or statistical modeling as a mutually exclusive decision tree. Instead, selected approaches from both areas should be considered as both types of methodologies are based on the same mathematical principles but expressed somewhat differently [5, 10].
The authors declare no conflict of interest.
Authors work for Symphony Health, ICON plc Organization.
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Antioxidant compounds act through several chemical mechanisms: hydrogen atom transfer (HAT), single electron transfer (SET), and the ability to chelate transition metals. The importance of antioxidant mechanisms is to understand the biological meaning of antioxidants, their possible uses, their production by organic synthesis or biotechnological methods, or for the standardization of the determination of antioxidant activity. In general, antioxidant molecules can react either by multiple mechanisms or by a predominant mechanism. The chemical structure of the antioxidant substance allows understanding of the antioxidant reaction mechanism. This chapter reviews the in vitro antioxidant reaction mechanisms of organic compounds polyphenols, carotenoids, and vitamins C against free radicals (FR) and prooxidant compounds under diverse conditions, as well as the most commonly used methods to evaluate the antioxidant activity of these compounds according to the mechanism involved in the reaction with free radicals and the methods of in vitro antioxidant evaluation that are used frequently depending on the reaction mechanism of the antioxidant.",book:{id:"8008",slug:"antioxidants",title:"Antioxidants",fullTitle:"Antioxidants"},signatures:"Norma Francenia Santos-Sánchez, Raúl Salas-Coronado, Claudia Villanueva-Cañongo and Beatriz Hernández-Carlos",authors:[{id:"143354",title:"Dr.",name:"Raúl",middleName:null,surname:"Salas-Coronado",slug:"raul-salas-coronado",fullName:"Raúl Salas-Coronado"},{id:"148546",title:"Dr.",name:"Norma Francenia",middleName:null,surname:"Santos-Sánchez",slug:"norma-francenia-santos-sanchez",fullName:"Norma Francenia Santos-Sánchez"},{id:"193718",title:"Dr.",name:"Beatriz",middleName:null,surname:"Hernández-Carlos",slug:"beatriz-hernandez-carlos",fullName:"Beatriz Hernández-Carlos"},{id:"278133",title:"Dr.",name:"Claudia",middleName:null,surname:"Villanueva-Cañongo",slug:"claudia-villanueva-canongo",fullName:"Claudia Villanueva-Cañongo"}]},{id:"65331",doi:"10.5772/intechopen.83731",title:"Flavonoids and Phenolic Acids as Potential Natural Antioxidants",slug:"flavonoids-and-phenolic-acids-as-potential-natural-antioxidants",totalDownloads:2757,totalCrossrefCites:35,totalDimensionsCites:78,abstract:"For centuries, aromatic herbs and spices have been added to different foods to improve the flavor and organoleptic properties. The use of aromatic plants and spices in phytotherapy is mostly related to different activities of their essential oils, such as antimicrobial, spasmolytic, carminative, hepatoprotective, antiviral, and anticarcinogenic activities. Furthermore, many studies point to strong antioxidant activities of aromatic plants and their essential oils. Knowing that phenolic compounds are the most responsible for the antioxidant activity, the amount of total phenolic contents and content of flavonoids have also been determined. In order to examine the antioxidant properties of five different extracts of Laurus nobilis L. leaves, various assays which measure free radical scavenging ability were carried out: 1,1-diphenyl-2-picrylhydrazyl, hydroxyl, superoxide anion, nitric oxide and hydroxyl radical scavenger capacity test, and lipid peroxidation assay. In all of the tests, only the EtOAc extract showed a potent antioxidant effect.",book:{id:"8008",slug:"antioxidants",title:"Antioxidants",fullTitle:"Antioxidants"},signatures:"Biljana Kaurinovic and Djendji Vastag",authors:[{id:"142369",title:"Prof.",name:"Biljana",middleName:null,surname:"Kaurinovic",slug:"biljana-kaurinovic",fullName:"Biljana Kaurinovic"},{id:"286918",title:"Prof.",name:"Djendji",middleName:null,surname:"Vastag",slug:"djendji-vastag",fullName:"Djendji Vastag"}]},{id:"65225",doi:"10.5772/intechopen.83544",title:"Antioxidant Categories and Mode of Action",slug:"antioxidant-categories-and-mode-of-action",totalDownloads:2330,totalCrossrefCites:7,totalDimensionsCites:20,abstract:"Oxidative stress has received a considerable scientific attention as a mediator in the etiology of many human diseases. Oxidative stress is the result of an imbalance between free radicals and antioxidants. Cells can be damaged by free radicals that are considered to play a main role in the aging process and diseases development. Antioxidants are the first line of defense against the detrimental effects of free radical damage, and it is essential to maintain optimal health via different mechanisms of action. Types of antioxidants range from those generated endogenously by the body cells, to exogenous agents such as dietary supplements. Antioxidant insufficiency can be developed as a result of decreased antioxidant intake, synthesis of endogenous enzymes, or increased antioxidant utilization. To maintain optimal body function, antioxidant supplementation has become an increasingly popular practice through improving free radical protection. In this chapter, we first elucidate the oxidative stress, and then define the antioxidant and its categories. Finally, introduce the antioxidants mode of actions for cell protection from free radicals.",book:{id:"8008",slug:"antioxidants",title:"Antioxidants",fullTitle:"Antioxidants"},signatures:"Manal Azat Aziz, Abdulkareem Shehab Diab and Abeer Abdulrazak Mohammed",authors:[{id:"276717",title:"Associate Prof.",name:"Manal",middleName:null,surname:"Azat Aziz",slug:"manal-azat-aziz",fullName:"Manal Azat Aziz"},{id:"286369",title:"Dr.",name:"Abdulkareem",middleName:null,surname:"Shehab Diab",slug:"abdulkareem-shehab-diab",fullName:"Abdulkareem Shehab Diab"},{id:"312155",title:"Dr.",name:"Abeer Abdulrazak",middleName:null,surname:"Mohammed",slug:"abeer-abdulrazak-mohammed",fullName:"Abeer Abdulrazak Mohammed"}]},{id:"66504",doi:"10.5772/intechopen.85468",title:"Antioxidants as a Double-Edged Sword in the Treatment of Cancer",slug:"antioxidants-as-a-double-edged-sword-in-the-treatment-of-cancer",totalDownloads:1650,totalCrossrefCites:13,totalDimensionsCites:18,abstract:"Antioxidant compounds are thought to prevent and treat diseases, especially cancer, under any circumstances. For this purpose, nature-based antioxidants nowadays are being commonly used to prevent and treat diseases. Indeed, phenolic compounds found in medicinal plants have opened a new horizon to prevent and treat diseases because of having antioxidant properties. However, some recent studies have reported that antioxidants are not absolute anticancer compounds and certain drugs have been reported to reduce levels of reactive oxygen species (ROS) in the cancer cells, i.e., their main action mechanism. It has been argued that increasing levels of ROS cause an increase in apoptosis rate and therefore can be considered an approach to treat fatal and hard-to-treat cancers. This chapter seeks to partly explain the role of ROS in progression or inhibition of cancer growth in addition to the role of antioxidants in preventing and treating this disease.",book:{id:"8008",slug:"antioxidants",title:"Antioxidants",fullTitle:"Antioxidants"},signatures:"Majid Asadi-Samani, Najmeh Kaffash Farkhad, Mohammad Reza Mahmoudian-Sani and Hedayatollah Shirzad",authors:[{id:"237542",title:"Dr.",name:"Majid",middleName:null,surname:"Asadi-Samani",slug:"majid-asadi-samani",fullName:"Majid Asadi-Samani"},{id:"275475",title:"Dr.",name:"Mohammad",middleName:null,surname:"Reza Mahmoudian-Sani",slug:"mohammad-reza-mahmoudian-sani",fullName:"Mohammad Reza Mahmoudian-Sani"},{id:"295230",title:"Dr.",name:"Najmeh",middleName:null,surname:"Kafash Farkhad",slug:"najmeh-kafash-farkhad",fullName:"Najmeh Kafash Farkhad"},{id:"295231",title:"Dr.",name:"Hedayatollah",middleName:null,surname:"Shirzad",slug:"hedayatollah-shirzad",fullName:"Hedayatollah Shirzad"}]},{id:"65067",doi:"10.5772/intechopen.82879",title:"Diseases Related to Types of Free Radicals",slug:"diseases-related-to-types-of-free-radicals",totalDownloads:1821,totalCrossrefCites:9,totalDimensionsCites:13,abstract:"The free radicals are reactive molecules with electron-rich groups produced during metabolic reactions occurring in the cells. These free radicals are collectively known as reactive oxygen species (ROS) and reactive nitrogen species (RNS). Lipid peroxidation products and protein carbonyls species are under the group of ROS, and nitric oxide and peroxynitrites are under the group of RNS. The malondialdehyde that reacts with LDL-C indirectly induced the risk of atherosclerosis. The protein carbonyls acts as marker of protein oxidation and exerts damage to proteins. The nitric oxide plays an important role in DNA damage, inflammation, proliferation of cancer cells, and dysfunction of apoptosis. The peroxynitrites could induce the process of lipid peroxidation, DNA damage, and may exert chronic damage to all biomolecules. The aim of the present study is that the free radicals may react with biomolecules of the cells and play an important role in the development of chronic disease conditions in the humans.",book:{id:"8008",slug:"antioxidants",title:"Antioxidants",fullTitle:"Antioxidants"},signatures:"Narendra Maddu",authors:[{id:"272889",title:"Dr.",name:"Narendra",middleName:null,surname:"Maddu",slug:"narendra-maddu",fullName:"Narendra Maddu"}]}],mostDownloadedChaptersLast30Days:[{id:"66259",title:"Antioxidant Compounds and Their Antioxidant Mechanism",slug:"antioxidant-compounds-and-their-antioxidant-mechanism",totalDownloads:7587,totalCrossrefCites:58,totalDimensionsCites:152,abstract:"An antioxidant is a substance that at low concentrations delays or prevents oxidation of a substrate. Antioxidant compounds act through several chemical mechanisms: hydrogen atom transfer (HAT), single electron transfer (SET), and the ability to chelate transition metals. The importance of antioxidant mechanisms is to understand the biological meaning of antioxidants, their possible uses, their production by organic synthesis or biotechnological methods, or for the standardization of the determination of antioxidant activity. In general, antioxidant molecules can react either by multiple mechanisms or by a predominant mechanism. The chemical structure of the antioxidant substance allows understanding of the antioxidant reaction mechanism. This chapter reviews the in vitro antioxidant reaction mechanisms of organic compounds polyphenols, carotenoids, and vitamins C against free radicals (FR) and prooxidant compounds under diverse conditions, as well as the most commonly used methods to evaluate the antioxidant activity of these compounds according to the mechanism involved in the reaction with free radicals and the methods of in vitro antioxidant evaluation that are used frequently depending on the reaction mechanism of the antioxidant.",book:{id:"8008",slug:"antioxidants",title:"Antioxidants",fullTitle:"Antioxidants"},signatures:"Norma Francenia Santos-Sánchez, Raúl Salas-Coronado, Claudia Villanueva-Cañongo and Beatriz Hernández-Carlos",authors:[{id:"143354",title:"Dr.",name:"Raúl",middleName:null,surname:"Salas-Coronado",slug:"raul-salas-coronado",fullName:"Raúl Salas-Coronado"},{id:"148546",title:"Dr.",name:"Norma Francenia",middleName:null,surname:"Santos-Sánchez",slug:"norma-francenia-santos-sanchez",fullName:"Norma Francenia Santos-Sánchez"},{id:"193718",title:"Dr.",name:"Beatriz",middleName:null,surname:"Hernández-Carlos",slug:"beatriz-hernandez-carlos",fullName:"Beatriz Hernández-Carlos"},{id:"278133",title:"Dr.",name:"Claudia",middleName:null,surname:"Villanueva-Cañongo",slug:"claudia-villanueva-canongo",fullName:"Claudia Villanueva-Cañongo"}]},{id:"65225",title:"Antioxidant Categories and Mode of Action",slug:"antioxidant-categories-and-mode-of-action",totalDownloads:2329,totalCrossrefCites:7,totalDimensionsCites:20,abstract:"Oxidative stress has received a considerable scientific attention as a mediator in the etiology of many human diseases. Oxidative stress is the result of an imbalance between free radicals and antioxidants. Cells can be damaged by free radicals that are considered to play a main role in the aging process and diseases development. Antioxidants are the first line of defense against the detrimental effects of free radical damage, and it is essential to maintain optimal health via different mechanisms of action. Types of antioxidants range from those generated endogenously by the body cells, to exogenous agents such as dietary supplements. Antioxidant insufficiency can be developed as a result of decreased antioxidant intake, synthesis of endogenous enzymes, or increased antioxidant utilization. To maintain optimal body function, antioxidant supplementation has become an increasingly popular practice through improving free radical protection. In this chapter, we first elucidate the oxidative stress, and then define the antioxidant and its categories. Finally, introduce the antioxidants mode of actions for cell protection from free radicals.",book:{id:"8008",slug:"antioxidants",title:"Antioxidants",fullTitle:"Antioxidants"},signatures:"Manal Azat Aziz, Abdulkareem Shehab Diab and Abeer Abdulrazak Mohammed",authors:[{id:"276717",title:"Associate Prof.",name:"Manal",middleName:null,surname:"Azat Aziz",slug:"manal-azat-aziz",fullName:"Manal Azat Aziz"},{id:"286369",title:"Dr.",name:"Abdulkareem",middleName:null,surname:"Shehab Diab",slug:"abdulkareem-shehab-diab",fullName:"Abdulkareem Shehab Diab"},{id:"312155",title:"Dr.",name:"Abeer Abdulrazak",middleName:null,surname:"Mohammed",slug:"abeer-abdulrazak-mohammed",fullName:"Abeer Abdulrazak Mohammed"}]},{id:"62579",title:"Where and How in the mTOR Pathway Inhibitors Fight Aging: Rapamycin, Resveratrol, and Metformin",slug:"where-and-how-in-the-mtor-pathway-inhibitors-fight-aging-rapamycin-resveratrol-and-metformin",totalDownloads:2663,totalCrossrefCites:3,totalDimensionsCites:3,abstract:"The molecular mechanisms underlying the quality and quantity of life extension appear to sometimes be orthogonal. For example, while resveratrol has continued to prove beneficial in reducing obesity, it has had less efficacy in extending lifespan. On the other hand, rapamycin and the chemically similar rapalogs extend lifespan across genera of life from yeast, to nematodes, to mice. Caloric restriction (CR) and bioavailable small molecules, which mimic a fasted state, upregulate autophagy, catabolism of fats over anabolism of carbohydrates, and decrease oxidative stress and inflammation. CR mimics are currently being investigated to elucidate the best dosage, route of administration, timing in life, where best to inhibit in the mTOR pathway, and effects of long-term use on mTORC1 verse mTORC2 complexes. Comparisons between rapamycin, resveratrol, and metformin targets, downstream pathway effects, dosage, and clinical trials will be discussed.",book:{id:"7258",slug:"resveratrol-adding-life-to-years-not-adding-years-to-life",title:"Resveratrol",fullTitle:"Resveratrol - Adding Life to Years, Not Adding Years to Life"},signatures:"Sage Arbor",authors:[{id:"245319",title:"Ph.D.",name:"Sage",middleName:null,surname:"Arbor",slug:"sage-arbor",fullName:"Sage Arbor"}]},{id:"65331",title:"Flavonoids and Phenolic Acids as Potential Natural Antioxidants",slug:"flavonoids-and-phenolic-acids-as-potential-natural-antioxidants",totalDownloads:2755,totalCrossrefCites:35,totalDimensionsCites:78,abstract:"For centuries, aromatic herbs and spices have been added to different foods to improve the flavor and organoleptic properties. The use of aromatic plants and spices in phytotherapy is mostly related to different activities of their essential oils, such as antimicrobial, spasmolytic, carminative, hepatoprotective, antiviral, and anticarcinogenic activities. Furthermore, many studies point to strong antioxidant activities of aromatic plants and their essential oils. Knowing that phenolic compounds are the most responsible for the antioxidant activity, the amount of total phenolic contents and content of flavonoids have also been determined. In order to examine the antioxidant properties of five different extracts of Laurus nobilis L. leaves, various assays which measure free radical scavenging ability were carried out: 1,1-diphenyl-2-picrylhydrazyl, hydroxyl, superoxide anion, nitric oxide and hydroxyl radical scavenger capacity test, and lipid peroxidation assay. In all of the tests, only the EtOAc extract showed a potent antioxidant effect.",book:{id:"8008",slug:"antioxidants",title:"Antioxidants",fullTitle:"Antioxidants"},signatures:"Biljana Kaurinovic and Djendji Vastag",authors:[{id:"142369",title:"Prof.",name:"Biljana",middleName:null,surname:"Kaurinovic",slug:"biljana-kaurinovic",fullName:"Biljana Kaurinovic"},{id:"286918",title:"Prof.",name:"Djendji",middleName:null,surname:"Vastag",slug:"djendji-vastag",fullName:"Djendji Vastag"}]},{id:"66161",title:"Antioxidants: Natural Antibiotics",slug:"antioxidants-natural-antibiotics",totalDownloads:1607,totalCrossrefCites:5,totalDimensionsCites:8,abstract:"The aim of this current piece of writing is to draw the attention of readers and researchers toward the natural antioxidants that can take the place of synthetic antibiotics to avoid bacterial resistance and gastrotoxicity/nephrotoxicity. Antioxidants such as polyphenols, vitamins, and carotenoids are the organic compounds mainly extracted from natural sources and dominantly involved in boosting the defense system of organisms. The main public health-related issue over the globe is ever-growing bacterial resistance to synthetic antibiotics, which is being continuously reported during the last decade. Further, the pipeline of the development of new synthetic antibacterial agents to replace the resistant antibiotics in clinical set-up is gradually drying up. This scenario originated the concept to revive the interest toward natural antibacterial products due to their chemical diversity, which provide important therapeutic effect and make the microbes unable to copy them for creating resistance. Natural products, especially polyphenols had been seen in antioxidant, antibacterial, anticancer, anti-inflammation, and antiviral activities with encouraging results. In this chapter, we will focus over the role of natural antioxidants as antibacterial agents.",book:{id:"8008",slug:"antioxidants",title:"Antioxidants",fullTitle:"Antioxidants"},signatures:"Syed Ali Raza Naqvi, Sana Nadeem, Sana Komal, Syed Ali Asad Naqvi, Muhammad Samee Mubarik, Sajid Yaqub Qureshi, Shahzad Ahmad, Ali Abbas, Muhammad Zahid, Naeem-Ul-Haq Khan, Syed Shujat Raza and Nosheen Aslam",authors:[{id:"259190",title:"Dr.",name:"Syed Ali Raza",middleName:null,surname:"Naqvi",slug:"syed-ali-raza-naqvi",fullName:"Syed Ali Raza Naqvi"},{id:"287649",title:"Ms.",name:"Sana",middleName:null,surname:"Nadeem",slug:"sana-nadeem",fullName:"Sana Nadeem"},{id:"287650",title:"Ms.",name:"Sana",middleName:null,surname:"Komal",slug:"sana-komal",fullName:"Sana Komal"},{id:"287652",title:"Dr.",name:"Muhammad Samee",middleName:null,surname:"Mubarik",slug:"muhammad-samee-mubarik",fullName:"Muhammad Samee 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