Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\n
Thank you all for being part of the journey. 5,000 times thank you!
\\n\\n
Now with 5,000 titles available Open Access, which one will you read next?
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n
"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\n\n
Thank you all for being part of the journey. 5,000 times thank you!
\n\n
Now with 5,000 titles available Open Access, which one will you read next?
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1. Introduction
The use of specific chemicals to treat specific diseases and disorders dates to 1910 when Paul Ehrlich and Sahachiro Hata discovered that salvarsan, also known as arsphenamine and compound 606, killed the microorganism that caused syphilis. Their research relied on animal models of syphilis as, even currently, syphilis cannot be grown in culture medium. Arsphenamine was the first synthetic drug to actually target and kill a disease-causing organism and is credited with starting the pharmaceutical age. Ehrlich is also credited with coining the term magic bullet in reference to a drug that would kill a microorganism without damaging or otherwise affecting the host of the microorganism: the patient. As I will explain, despite being an inspirational concept that led to advances in science and medicine, the notion of a magic bullet proved incomplete. Salvarsan and Ehrlich’s concept of a magic bullet are important to current concepts in drug testing because: 1) salvarsan was initially called compound 606 as it was the 606th compound tested on animals in an attempt to find a treatment for syphilis; and 2) the concept of a magic bullet was based on the scientific process known as reductionism. In this chapter, I will explore the reductionist approach of using animal models in drug development, especially in toxicity testing.
2. Reductionism and complexity
The use of animals as models for human anatomy and pathophysiology dates back millennia but the modern version began with Claude Bernard in the 19th century. Bernard was a firm believer in the reductionist approach to medical science and that approach has indeed served biomedical science well for decades. A review of reductionism will allow us to contrast this approach to understanding the material universe with systems biology, which is needed in order to fully understand complex living systems. [1-13]
Ernst Mayr defines reductionism as: “The belief that the higher levels of integration of a complex system can be fully explained through a knowledge of the smallest components.”[[14] p290] For example, physics attempts to describe the universe in terms of a few elementary particles, and the relationships among them. Reductionism has been very successful in describing many aspects of the material universe, including allowing successful predictions to be made. Reductionism is associated with Newton, Descartes, and determinism and the reliance on animal models in medical science arose during the time of Newtonian physics vis-à-vis reductionism and determinism. Newton said: “Therefore to the same natural effects we must, as far as possible, assign the same causes” and went on to explain that this rule applies “to respiration in a man and in a beast, the descent of stones in Europe and America, the light of our culinary fire and of the sun, the reflection of light in the earth and in the planets.”[[15] p3-5] Both Newton and Claude Bernard subscribed to the position that similar causes yield similar effects. Indeed, this concept was one of the breakthroughs that led to the systematic method of inquiry known as the resoluto-compositive method or method of analysis and synthesis. This concept of causal determinism rests on two claims. First, all events have causes, and second, for qualitatively identical systems, the same cause is followed by the same effect. Causal determinism is a presupposition of much scientific activity. The idea that results in the laboratory can be extended to form expectations about qualitatively similar systems outside the laboratory is embodied in this idea, as is the claim that experiments should be replicable. [16] This was how science viewed the universe, including animate bodies, when the animal model was embraced by science in the 19th century.
Claude Bernard was a strict causal determinist, meaning that if X caused Y in a monkey it was also cause Y in a human. Bernard stated: “Physiologists... deal with just one thing, the properties of living matter and the mechanism of life, in whatever form it shows itself. For them genus, species and class no longer exist. There are only living beings; and if they choose one of them for study, that is usually for convenience in experimentation.”[[17] p 111] Further complicating matters, Bernard and many of his colleagues rejected the notion of evolution put forth by Darwin. [17-19] Bernard thought that organs and other tissues were interchangeable among animals and that all differences could be accounted for based on scaling; the chief difference between humans and animals being a soul.[19] This thinking persists even in recent times as exemplified by the baboon heart transplant in to the recipient Baby Fae, performed by the creationist surgeon Leonard Bailey of Loma Linda University in 1984. [[20] p162-3]
However, recent advances in other disciplines of science, namely chaos and complexity along with evolutionary biology, have called into question the use of reductionism as the sole factor in studying complex systems. Moreover, the developments in evolutionary biology and genetics are cause for further concern regarding the use of one complex evolved system, say a mouse, to predict responses to perturbations such as disease and drugs for another differently evolved complex system, say a human. For example, we now understand that the same gene can be used in different ways among species and that knocking out a gene in one species is not predictive for the function of that gene in another species.[21-27] This has implications for drug development.
Reductionism was used to study simply systems as opposed to complex systems. Animals, including humans, are complex systems and as such exhibit the characteristics listed below [from [28]].
Complex systems are robust, meaning they have the capacity to resist change. [8, 9, 29-35] This can be illustrated by the fact that knocking out a gene in one strain of mouse may produce no noticeable effects.
Redundancy tends be a part of complex systems and may explain some aspects of robustness. For example, many members of the kingdom Animalia exhibit gene redundancy. [8, 9, 29-35]
Different parts of a complex system are linked to and affect one another in a synergistic manner. In other words, there is positive and negative feedback in a complex system. [36] This is why overloading one part of a complex system with say vitamins, may not result in a healthier individual. The feedback system results in the rest of the system acting to simply excrete the unneeded vitamins.
Complex systems are also modular. But failure in one module does not necessarily spread to the system as a whole as redundancy and robustness also exist. [37-40]
The modules do communicate though. For example, genes tend to be part of networks, genes interact with proteins, proteins interact with other proteins and so on.
Complex systems communicate with their environment—are dynamic. [37-40]
Complex systems are very dependent upon initial conditions. [39] For example, very small changes in genetic makeup can result in dramatic differences in response to perturbations of the living system.
The causes and effects of the events that a complex system experiences are not proportional to each other. Perturbations to the system have effects that are nonlinear, in other words large perturbations may result in no change while small perturbations may cause havoc. [37-40]
The whole is greater than the sum of the parts. [1, 8, 9, 30, 39]
Complex systems have emergent properties. An emergent property cannot be predicted by full knowledge of the component parts. For example, the formation of a flock of birds and hurricanes are examples of emergent phenomenon as is perhaps consciousness. [39]
Reductionism is essentially divide and conquer. By dividing a system into its parts and ascertaining the functions of all the parts of the system, one can deduce the function of the entire system. The gears of a Swiss watch, for example, are capable of description on their own, without reference to the system from which they are removed. Conversely, the individual components of a complex system must be described based on the interaction of the parts. Describing individual components in isolation, regardless of how detailed such a description is, cannot fully describe the complex system as a whole. The whole is greater than the sum of the parts. A complex system must be described based on the organization of the individual components. [41, 42]
Miska states:
The basic analytical method that is behind most biomedical research can be traced back over 300 years to Descartes’s essay Discourse on Method, which argued that an animal is a clock-like machine in which the parts and their relationships to one another are precise and unchangeable, and in which causes and effects can be understood by taking the pieces apart. This so-called ‘reductionist’ approach to understanding biology and medicine has been very productive, but is now up against problems that require different frameworks, institutionally and intellectually. [43]\n\t\t\t\t
Nicolis & Prigogine defined complexity as the ability of a system “to switch between different modes of behavior as the environmental conditions are varied.”[44] In other words, complex systems are able to adapt to their environments just as life on this planet has adapted resulting in different species. But these adaptions mean that two complex systems that were originally identical would now be less similar and behave differently in certain circumstances. An example of this would be the susceptibility to disease between monozygotic twins. [45-56] Van Regenmortel states:
Reductionists tend to disregard the fact that all biological systems possess so-called emergent properties that arise through the multiple interconnections and relations existing between individual components of the system. These emergent, relational properties do not exist in the constituent parts and they cannot be deduced or predicted from the properties of the individual, isolated components [[57]p258]. Examples of emergent properties are the viscosity of water (individual water molecules have no viscosity), the colour of a chemical, a melody arising from notes, the saltiness of sodium chloride, the specificity of an antibody and the immunogenicity of an antigen. [58]\n\t\t\t\t
Living complex systems are the result of various evolutionary processes and as such are arguably the most complex of all complex systems. Species differ because of the presence of different genes, mutation in the same genes, a difference in the number of the same allele (copy number variants), the same genes may be regulated or expressed differently, alternative splicing, the presence of modifier or background genes, differences in gene networks and protein networks, and convergent evolution where two species share a trait but the trait evolved independently in each. Individuals of the same species may differ for many of the above reasons but also because of dissimilarities in environmental exposures. [50] Importantly, each of the above means that different species as well as individuals of the same species manifest differences in the initial conditions of their complex system. The above also translates into differences in other characteristics of a complex system such as robustness and redundancy.
The progress in these two areas of science, complexity science and evolutionary biology, results in strong theoretical concerns regarding the use of animals as predictive models in drug development. We should expect animals and humans to share responses to perturbations at the level of organization where complex systems can be described as simple systems but not for perturbations occurring at the level of organization where the system as a whole is studied or where parts of the systems that are themselves complex are studied. I will next examine the empirical evidence and place it in the context of these theoretical concerns.
3. Prediction in science
The third relevant advance in science since animal models were mandated for use in drug development is the formal evaluation of animal models in terms of their predictive value for humans. Animal models are used for ascertaining the properties of absorption, distribution, metabolism, elimination and toxicity (ADMET). As all of these properties influence toxicity, an examination of the ability of animal models to predict these properties is important, as is the straightforward examination of animal models for toxicity itself. The answer to the question of the predictive ability of animal models was hinted at by the fact that Ehrlich and Hata ultimately tested the 606th compound of a series in their attempt to find a treatment for syphilis. Previous compounds had successfully treated syphilis in animal models but had failed for various reasons in humans. Even salvarsan resulted in side effects in humans that were unforeseen in animal models.
The ability to predict facts about the material universe is a hallmark of science. Hypotheses are generated that make predictions about the phenomena under study and the success or failure of these predictions can falsify or strengthen the hypothesis. This use of the term predict differs from determining whether a modality, practice, or test is predictive for its purpose. For example, a CT scan of the chest is a predictive test for diagnosing a pneumothorax, because the CT scan, as opposed to a chest x-ray, is successful in locating the pneumothorax essentially 100% of the time. In order to evaluate a modality like CT scans, a blood test for cancer, or even the use of dogs for catching drug smugglers in airports, the calculations in table 1 are employed.
When evaluating the predictive value of methods, practices, or tests for use in biomedical science, positive predictive value (PPV) and negative predictive value (NPV) > 0.9 are sought. If a single test alone cannot yield such high values then a combination of tests can be evaluated in hopes that the combination will meet the criteria. Such evaluations have been made for toxicity testing using animal models as well as other animal model-based tests in drug development. Profound inter-species differences, as well as inter-individual human differences, have been revealed for absorption [[59] p 8-10] [[60] pp 5, 9, 45, 50, 66-7, 90, 102-3,] [61-64], distribution [65, 66], metabolism [67-77], elimination [78, 79], and toxicity [64, 80-91], which results in predictive values for these animal models that are far below those required in biomedical science. For example, Litchfield conducted a classic study in 1962 comparing toxicity among three species: humans, rats, and dogs. The positive predictive values for the animal models were between 0.49 and 0.55. [92] Similarly, Suter compared toxicities for ergoloid mesylates, bromocriptine, ketotifen, cyclosporine, FK 33-824, and clozapine in animals and humans. The sensitivity for toxicity for the animal tests was 0.52 and the predictive value positive was 0.31. [93] Fourches et al. evaluated animal human data for 1061 compounds known to cause hepatotoxicity in humans and found that the concordance or sensitivity among species was around 39-44%.[94] The positive and negative predictive values could not be calculated from the article but would be well below 0.39. Smith and Caldwell studied twenty-three chemicals and discovered that only four were metabolized the same in humans and rats. [70] Sietsema [95], compared the oral bioavailability of 400 drugs in humans with three other species (see Figure 1) and concluded the data was consistent with a “scatter-gram.” Similar results have been obtained from other studies.[84, 96-101]
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t\tGold Standard\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t\tGS+\n\t\t\t
\n\t\t\t
\n\t\t\t\tGS-\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t\tTest\n\t\t\t
\n\t\t\t
\n\t\t\t\tT+\n\t\t\t
\n\t\t\t
\n\t\t\t\tTP\n\t\t\t
\n\t\t\t
\n\t\t\t\tFP\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t\tT-\n\t\t\t
\n\t\t\t
\n\t\t\t\tFN\n\t\t\t
\n\t\t\t
\n\t\t\t\tTN\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
T+ = Test positive T- = Test negative T = True F = False P = Positive N = Negative GS+ = Gold standard positive GS- = Gold standard negative
\n\t\t
\n\t\t
\n\t\t\t
Sensitivity = TP/(TP+FN)
\n\t\t
\n\t\t
\n\t\t\t
Specificity = TN/(FP+TN)
\n\t\t
\n\t\t
\n\t\t\t
Positive Predictive Value = TP/(TP+FP)
\n\t\t
\n\t\t
\n\t\t\t
Negative Predictive Value = TN/(FN+TN)
\n\t\t
\n\t
Table 1.
Binomial classification method for calculating sensitivity, specificity, positive predictive value, and negative predictive value when comparing a modality, practice, or test with a gold standard.
Figure 1.
Variation in bioavailability among species. (Based on data from [95].)
The fact that animal models lack predictive ability is well known.[102-110] This shortcoming includes the inability of animal models to be predictive modalities for carcinogenicity.[111, 112] Salsburg stated: “Thus the lifetime feeding study in mice and rats appears to have less than a 50% probability of finding known human carcinogens. On the basis of probability theory, we would have been better off to toss a coin...”[111]
The general attitude in the drug development-related sciences reflects the empirical evidence. Cook et al:
Over many years now there has been a poor correlation between preclinical therapeutic findings and the eventual efficacy of these [anti-cancer] compounds in clinical trials [109, 110].... The development of antineoplastics is a large investment by the private and public sectors, however, the limited availability of predictive preclinical systems obscures our ability to select the therapeutics that might succeed or fail during clinical investigation. [108]
Reuters quoted Francis Collins, Director of the NIH, as stating that: “about half of drugs that work in animals may turn out to be toxic for people. And some drugs may in fact work in people even if they fail in animals, meaning potentially important medicines could be rejected.”[113] Alan Oliff, former executive director for cancer research at Merck Research Laboratories in West Point, Pennsylvania asserted in 1997: “The fundamental problem in drug discovery for cancer is that the [animal] model systems are not predictive at all.”[114] Björquist and Sartipy stated: “Furthermore, the compound attrition rate is negatively affected by the inability to predict toxicity and efficacy in humans. These shortcomings are in turn caused by the use of experimental pre-clinical model systems that have a limited human clinical relevance...”[115] In 2006, then U.S. Secretary of Health and Human Services Mike Leavitt declared: “Currently, nine out of ten experimental drugs fail in clinical studies because we cannot accurately predict how they will behave in people based on laboratory and animal studies.”[116] Zielinska, writing in The Scientist supported the above, stating:
Mouse models that use transplants of human cancer have not had a great track record of predicting human responses to treatment in the clinic. It’s been estimated that cancer drugs that enter clinical testing have a 95 percent rate of failing to make it to market, in comparison to the 89 percent failure rate for all therapies... Indeed, “we had loads of models that were not predictive, that were [in fact] seriously misleading,” says NCI’s Marks, also head of the Mouse Models of Human Cancers Consortium... [117]
The inability of animal models to predict human response has also increased the cost of drug development as the cost for the 90-95% of drugs that fail must be recouped from the ones that go to market.[91, 118-121] Lost revenue has also resulted from the drugs that would have been marketable had animal models not derailed them in development. This lack of predictive ability for animal models is largely to blame for the cost of new medications and for the fact that the drug development pipeline is drying up.[115, 122, 123] Because animal models fail to predict drugs destined to fail, these drugs go to clinical trials and marketing which consumes roughly 95% of the cost for drug development.[124, 125] Catherine Shaffer, Contributing Editor of Drug Discovery & Development, wrote in 2012: “Drug development is an extremely costly endeavor. Estimates of the total expense of advancing a new drug from the chemistry stage to the market are as high as $2 billion. Much of that cost is attributable to drug failures late in development, after huge investments have been made. Drugs are equally likely to fail at that stage for safety reasons, as for a lack of efficacy, which is often well-established by the time large trials are launched.”[120] Roy estimates the real cost is even higher: “The true amount that companies spend per drug approved is almost certainly even larger today. Matthew Herper of Forbes recently totaled R&D spending from the 12 leading pharmaceutical companies from 1997 to 2011, and found that they had spent $802 billion to gain approval for just 139 drugs: a staggering $5.8 billion per drug.”[125] Kenneth Kaitin, director of Tuft’s Center for the Study of Drug commenting on Pharma’s drying pipeline in the March 7, 2011 New York Times, stated: “This is panic time, this is truly panic time for the industry.” Even when a drug does reach the market, there is a great amount of uncertainty regarding safety. For example, over 1000 drugs that reached the market were discovered to result in hepatotoxicity. [126]
Kirschner addressed this issue, asking: “could we develop a better way of predicting whether a drug will work or have intolerable side effects?” He then explains the problem in terms similar to what I have presented above:
In part, this problem stems from the fact that we rarely have a situation in which one gene can be linked to one disease and targeted by one drug. The nature of our biological system is that we have relatively few genes — say 20,000 basic core genes — that are used over and over again in different contexts. So when we investigate targets, we need to better appreciate how these function in different contexts. Moreover, there are many overlapping and redundant pathways, so we need to better understand genes not as individual elements with individual functions but within the context of the circuits in which they operate. This approach requires not just a wiring diagram, but a quantitative wiring diagram... [127]
4. This leads us to current efforts at improving drug development
4.1. Twenty-first century science
Today we have options for drug development and toxicity testing that did not exist until the 21st century, for example microdosing and pharmacogenomics. Two points need be emphasized before I address these two advances, however. First, animal models fail to meet the ends for which they are used; they are not predictive modalities for human response. Therefore using animal models is akin to relying on bloodletting as a treatment for cancer when oncologists have no cures for the cancer in question. Just as bloodletting is not effective as a treatment for cancer, regardless of whether or not other options are available, so employing animal models as they are currently utilized is nonsensical.
Second, technology is available, or is being developed, that will at least predict human response for certain properties important in drug development. However, regardless of how much time is needed in order for these technologies to be developed, animal models are simply ineffective and hence should be abandoned. Lack of effective technology does not justify the utilization of methods proven to be ineffective. Regardless of the technologies available, drug development must be human-based both when reductionism is used and when complexity is relevant. Basing drug development decisions on drug targets identified from animal models has not been effective. Human tissues can be studied instead and this will allow targets to be established in a more reliable manner. Humans must also be studied when responses to drugs are occurring at higher levels of organization; where the system is complex.
In 2006, the FDA approved microdosing for Phase 0 clinical trials.[128, 129] Microdosing is the process whereby very small doses of a drug are administered to human volunteers after which positron emission tomography (PET) and accelerator mass spectrometry (AMS) are used to assess pharmacokinetic (PK) data.[130-132] While animal models are used to inform the dose for the first administration of the drug, the usual range for drugs is 100ng to 100μg. If all drugs were initially administered at a dose of 1ng and subsequently increased, this would obviate the use of unreliable animal models and ensure that the first-in-human dose was lower than the most toxic substance currently known.[133, 134] This would be a reliably safe method for conducing first-in-human trials. Although in practice microdosing is currently only used to evaluate PK (as opposed to pharmacodynamics, which is abbreviated as PD), it could be used for evaluating the other properties of interest. For example, by increasing the dose incrementally, the drug could be evaluated for toxicity. This solves the problem of unanticipated catastrophic reactions such as occurred in the TGN1412 trial [135] and allows toxicity to be determined very early in the drug development process. Long term carcinogenicity studies could not be conducted in this fashion however animal models are not predictive for carcinogenicity and human data from long terms use is the de facto method now used. Nothing would be lost by eliminating long-term carcinogenicity studies in animals until predictive technologies are developed. According to the Centers for Disease Control and Prevention (CDC): “Most of what we know about chemicals and cancer in humans comes from scientists\' observation of workers. The most significant exposures to cancer-causing chemicals have occurred in workplaces where large amounts of toxic chemicals have been used regularly.”[136]
The concept of microdosing, used in combination with pharmacogenomics (see below) would allow go-no go decisions to be made early and reliably in drug development as well as matching drug to patient. The transition to full-scale clinical trials would also be seamless. As the dose was increased, an evaluation of efficacy could be made. By starting the dose at 1ng and increasing, the entire clinical trial could be conducted much more reliably and efficiently, drugs destined to fail could be eliminated earlier thus saving money, and the drugs could be matched to genotype before being marketed thus further saving money and decreasing side effects. This leads us to the concepts of pharmacogenomics and personalized medicine.
Personalized medicine seeks to individualize medicine both in terms of treatment and diagnosis while pharmacogenomics matches drugs to patients. Rashmi R Shah, previous Senior Clinical Assessor, Medicines and Healthcare products Regulatory Agency, London stated in 2005: “During the clinical use of a drug at present, a prescribing physician has no means of predicting the response of an individual patient to a given drug. Invariably, some patients fail to respond beneficially as expected whereas others experience adverse drug reactions (ADRs).”[137] Shah echoed comments by Allen Roses, then-worldwide vice-president of genetics at GlaxoSmithKline (GSK), who stated that fewer than half of the patients prescribed some of the most expensive drugs derived any benefit from them: “The vast majority of drugs - more than 90% - only work in 30 or 50% of the people.”[138] That individual humans respond very differently to disease and drugs [139, 140], including vaccines [141, 142], has long been appreciated. During the Korean War, Alving observed that black soldiers had an increased probability, compared with white soldiers, of developing anemia when from antimalarials. This was discovered to be secondary to a commonly occurring enzyme deficiency in the black soldiers.[143] Variation in disease susceptibility and response to drugs has been noted to exist between sexes [144-150] and ethnic groups [151-159] as well as between monozygotic twins.[45-52, 56]
Many advances have been made in linking drugs to genes, in part because of spin-offs from the Human Genome Project. Differences between humans including single nucleotide polymorphisms, copy number variants, differences the regulation and expression of the same genes, differences in gene networks, and the influence of background genes can result in a drug being efficacious for one patient but not another. Diseases vary intra-species as well. Michael Snyder, chair of genetics at Stanford University School of Medicine, recently stated: “However, the bulk of the differences among individuals are not found in the genes themselves, but in regions we know relatively little about. Now we see that these differences profoundly impact protein binding and gene expression.”[160, 161] Hunter et al studied a mouse model of cancer and discovered differences in metastatic efficiency secondary to background genes. Hunter et al:
Because all tumors were initiated by the same oncogenic event, differences in the metastasis microarray signature and metastatic potential are probably due to genetic background effects rather than different combinations of oncogenic mutations. Consistent with our observations in metastasis, several laboratories have shown similar strain differences with regard to oncogenesis, aging and fertility in transgenic mouse models.[162-164] Data on both primary tumors and metastases reinforce the notion that tumorigenesis and metastasis are complex phenotypes involving both inherent genetic components and cellular responses to extrinsic stimuli. [165]
Thein likewise stated: “As the defective genes for more and more genetic disorders become unravelled, it is clear that patients with apparently identical genotypes can have many different clinical conditions even in simple monogenic disorders.” Thein assessed β−thalassemia and noted that the clinical manifestations are very diverse, ranging from life threatening to asymptomatic. Thein: “The remarkable phenotypic diversity of the β−thalassemias is prototypical of how a wide spectrum of disease severity can be generated in single gene disorders.... relating phenotype to genotype is complicated by the complex interaction of the environment and other genetic factors at the secondary and tertiary levels...”[166]
Agarwal and Moorchung reinforce the above stating: “It is now increasingly apparent that modifier genes have a considerable role to play in phenotypic variations of single-gene disorders.” This is due to factors such as: “Oligogenic disorders occur because of a second gene modifying the action of a dominant gene. It is now certain that cancer occurs due to the action of the environment acting in combination with several genes.”[167] Friedman and Perrimon explain that there are “hundreds of potential regulators of known signaling pathways.”[168] PLoS Biology, in an editorial said the following about mouse models of autoimmune diseases: “These results fall in line with mounting evidence that background genes are not silent partners in gene-targeted disease models, but can themselves facilitate expression of the disease. This finding underscores the notion that genes are not solitary, static entities; their expression often depends on context. With genetically complex diseases, having the requisite combination of susceptibility genes does not always lead to disease.”[169]
Liu et al explain why the same genes can result in very outcomes:
A general view is that critical genes involved in biological pathways are highly conserved among species. To understand human autoimmune diseases, a great deal of effort has been devoted to the study of murine models that mirror many pathologic properties observed in the human disease. We have found that lymphocytes from humans with different autoimmune disease all carry a common conserved gene expression profile. Therefore, we wanted to determine if lymphocytes from common murine models of autoimmune disease carried a gene expression profile similar to the human profile and if both mouse models carried a shared gene expression profile. We identified numerous differentially expressed genes (DEGs) in the autoimmune strains compared to non-autoimmune strains. However, we found very little overlap in the gene expression profile between human autoimmune disease and murine models of autoimmune disease and between different murine autoimmune models. Our research further confirms that murine models of autoimmunity do not perfectly match human autoimmune diseases. [26]
Weiss et al continues this theme:
In contrast to these single gene effects, many drug treatment response phenotypes are complex, produced by multiple coding and regulatory variants in multiple genes that often interact in a signalling pathway. In these cases, each variant could contribute to the variance in the phenotype and there is no clear model of genetic inheritance. Genetic factors that influence whether a drug treatment response is complex include mode of inheritance (recessive versus dominant or additive); pleiotropy; incomplete penetrance; and epistasis, due to gene–environment interactions and environmental phenocopies. All of these factors contribute to the complexity of the response phenotypes. [170]
Gabor Miklos states:
There is enormous phenotypic variation in the extent of human cancer phenotypes, even among family members inheriting the same mutation in the adenomatous polyposis coli (APC) gene believed to be causal for colon cancer. In the experimental mouse knockout of the catalytic gamma subunit of the phosphatidyl-3-OH kinase, there can be a high incidence of colorectal carcinomas or no cancers at all, depending on the mouse strain in which the knockout is created, or into which the knockout is crossed... [27]
Because of advances alluded to above, society is seeing the death of the blockbuster and the arrival of the “niche buster.” [171] Herscu et al write: “The era of the \'blockbuster drug model\' is ending, and the development of personalized pharmaceutical system is on the rise.”[172] This is also due to the fact that diseases are being categorized into more types and individuals even within the same type react differently to drugs. Herscu et al write:
Diabetes mellitus, for example, was simply divided into juvenile or adult onset types for many years. Now we have pre-diabetes; Type I, broken into immune-related and other causes; Type 2, broken into secretory defect and insulin-resistant types; and more than 11 types that have been linked to specific genetic defects. However, even diabetic patients in a precisely defined category with shared genetic markers differ because they exist at different points along the continuum of the disease depending on their diet, exercise, comorbid conditions and other factors. These phenotypic dissimilarities are the source of inter-patient variability, which confounds both clinical trials and treatment results. [172]
Iressa was one of the first medications administered to patients based on genotype. Iressa did not perform well in clinical trials and was to be abandoned but clinicians were adamant that it helped some people with cancer. By genotyping the patients that responded well to Iressa, researchers were able to confirm that, in certain genotypes, Iressa was efficacious. Numerous drug responses have been matched to specific mutations. [77, 173-176] The Personalized Medicine Coalition notes that personalized medicine will allow patients and physicians to:
select optimal therapy and reduce "trial-and-error" medicine;
reduce adverse drug reactions;
improve the selection of drug targets;
increase patient compliance with therapy;
reduce the time, cost, and failure rate of clinical trials;
revive drugs that failed clinical trials or were withdrawn from the market;
avoid withdrawal of marketed drugs;
shift the emphasis in medicine from reaction to prevention; and
We are currently living in what will become known as the Age of Personalized Medicine. While much has yet to be discovered, society is already benefitting from personalized medicine applied to specific drugs and diseases. Contrast this with using a different species in an attempt to predict human response to drugs and disease. While animals can be used in basic science pursuits, empirical evidence from drug development, placed in the context of the scientific theories of Complexity and Evolution, demands that animal testing be replaced with human-based drug development. Implementing human-based testing early in the development process is how drugs should be developed now and it will be how drugs are developed in the future.
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\n
1. Background and introduction
\n
Recently, the space industry has pointed out that in the past 5 years, the commercial market has been driving the advancement of satellite technology. Lockheed Martin is building commercial satellites (e.g., Hellas-sat series) with advanced on-board processing capabilities for the Saudi Arabian [1]. Hellas satellites probably will be the first commercial HTS with a very advanced digital processor on-board. The focus of this chapter will be on commercial satellite systems for communication applications, and a comparison study between commercial HTS and typical satellites systems conducted by Inmarsat will be provided [2].
\n
For communication applications, commercial satellite systems have been categorized as mobile satellite services (MSSs), fixed satellite services (FSSs), broadcast satellite services (BSSs), and high-throughput satellite (HTS) services. Depending on the services, satellite payload architecture will be designed to meet the specified requirements for that service. Basically, satellite payload architecture can be classified into four categories: (1) analog bent-pipe satellite (ABPS); (2) digital bent-pipe satellite (DBPS); (3) advanced digital bent-pipe satellite using digital channelizer and beamformer (AdDBPS-DCB); and (4) advanced regenerative on-board processing satellite (AR-OBPS). This chapter provides an overview of these payload architectures and presents two satellite system architectures using AdBPS-DCBS and AR-OBPS payloads for the fifth-generation cellular phone (5G) applications.
\n
The chapter is organized as follows: Section 2 provides a comparison between commercial HTS and typical satellite systems; Section 3 discusses the typical satellite network topologies; Section 4 presents an overview of legacy ADPS transponder, existing DBPS transponder, AdBPS-DCBS transponder, and AR-OBPS satellite system; Section 5 discusses the use of AdBPS-DCBS transponder and AR-OBPS payloads for the fifth-generation cellular phone (5G) applications; and Section 6 concludes the chapter with a summary and brief discussion of way forward.
\n
\n
\n
2. Typical commercial satellites and HTS comparison
\n
Typical and regular commercial satellites are operating in C-band, Ku-band, and Ka-band with downlink frequencies approximately at 4, 12, and 40 GHz, respectively. For C-band, Ku-band, and Ka-band, the spectrum bandwidths available by geostationary orbital position are 500 MHz, 500 MHz, and 3.5 GHz, respectively. Typical antenna types for these regular commercial satellites are pointed antenna type with a single beam. Typical diameters for these pointed antennas are (a) greater than 1.8 m for C-band; (b) 0.9–1.2 m for Ku-band; and (c) 0.6–1.2 m for Ka-band satellite. Figure 1(a) illustrates a typical regular commercial satellite.
\n
Figure 1.
Typical commercial satellites and HTS configurations.
\n
Typical HTSs are usually also operating in Ku-band and Ka-band with the same downlink frequencies as the regular satellites except that they employ multiple pointed beam as oppose to a single-pointed beam. Figure 1(b) describes a multiple beam HTS system. The salient feature of multiple beams is the frequency reuse. The frequency reuse is defined as the number of times a satellite can reuse the same spectrum and frequencies. However, high frequency reuse factor can cause potential cochannel interference or an increase in carrier-to-interference power ratio (CIR or C/I). IMMARSAT has reported that a reuse factor of 5–30 is possible with multiple spot beams employed by commercial HTS. Depending on the number of beams implemented on-board of the satellite, the cost for HTS can be twice of the cost for a regular satellite. But, the cost per bit for HTS is much lower than the regular satellite. HTS is a preferred option for point-to-point services, for example, beyond line-of-sight (BLOS) cellular phone services. Table 1 provides a summary of the comparison of HTS and regular commercial satellites [2].
\n
\n
\n
\n
\n
\n\n
\n
Comparison factor
\n
Typical regular commercial satellite
\n
Typical high-throughput satellite (HTS)
\n
Remark
\n
\n\n\n
\n
Operational frequency band
\n
C-band, Ku-band, Ka-band
\n
Ku-band, Ka-band
\n
It should be noted that for data presented here, all satellites and supply are not equal; various technical, regulatory, and commercial parameters come into play when comparing the two-type satellites. Data collected from IMMARSAT. Source: see [2]
\n
\n
\n
Throughput capability (Gbps)
\n
~1–10
\n
~5–300+ (with frequency reuse in multiple spot beam)
\n
\n
\n
Typical cost including launch (USD)
\n
~200–300
\n
~300–500 (cost can be twice of regular satellite)
\n
\n
\n
Advantages
\n
Wide coverage; preferred solution for point-to-multipoint communication
\n
Higher bandwidth/lower cost per bit; preferred option for point-to-point services
\n
\n
\n
Disadvantages
\n
Limited supply available; lower spectrum efficiency for an equivalent frequency
\n
Higher upfront costs; difficult to find enough customers to fill each of the beams
\n
\n\n
Table 1.
Comparison of typical commercial satellites and HTS.
This section describes the most commonly used satellite network topologies, namely “Star” satellite network (Section 3.1) and “Mesh” satellite network (Section 3.2).
\n
\n
3.1 Typical “star” satellite network
\n
A typical commercial satellite network topology consists of an uplink from a central anchor station (aka satellite Gateway or satellite Hub) to a satellite and a downlink from the satellite to users. Users can be mobile or fixed users. Mobile users can be located in an airplane, a boat, or a car. Fixed users can be located in a building or a cellular base station. The “star” satellite network is derived from a spoke-hub distribution paradigm in computer networks, where one central hub serves as a conduit to transmit messages among network users [3]. Thus, for star satellite networks, all communications will be passed through a satellite gateway. As shown in Figure 2, if Mobile User 1 wants to talk to Mobile User 2, Mobile User 1 needs to send its messages to the satellite gateway (yellow lines), and satellite gateway relays that messages to Mobile User 2 (red lines).
\n
Figure 2.
Typical “star” satellite network.
\n
\n
\n
3.2 Typical “mesh” satellite network
\n
The “mesh” satellite network topology is derived from a local network topology, where the network nodes are corrected to each other directly, dynamically, and nonhierarchically to as many other nodes as possible [4]. In this network topology, the network nodes can cooperate with one another to route data from one user to another user efficiently. Hence, for mesh satellite network, Mobile User 1 can talk to fixed user directly without going through the satellite gateway (solid lines), and Mobile User 2 can also talk to the fixed user directly (dash lines). Any one of the user within the network can send the messages to a terrestrial network through the red lines representing uplink and downlink between the satellite gateway and the satellite (Figure 3).
\n
Figure 3.
Typical “mesh” satellite network.
\n
Star satellite network topology does not require advanced satellite payload processing on-board and multiple beam, but mesh satellite network requires advanced on-board processing and multiple beam allowing one user to communicate to another user automatically and effectively. Section 4 discusses various satellite payload architectures used in regular satellite and HTS for star and mesh satellite network applications.
\n
\n
\n
\n
4. Legacy, existing, and advanced satellite payload architectures
\n
This section presents an overview of legacy, existing, and advanced satellite payload architectures. Section 4.1 presents legacy ABPS payload architecture, Section 4.2 provides a description of a typical existing DBPS payload architecture, Section 4.3 discusses AdDBPS-DCB payload architecture, and Section 4.4 provides an overview of AR-OBPS payload architecture.
\n
\n
4.1 Legacy analog bent-pipe satellite (ABPS) payload architecture
\n
A typical legacy ABPS payload architecture is depicted in Figure 4, where the payload has multiple beam antennas (MBAs) using parabolic dishes. For this architecture, the RF signal is received at the satellite payload and amplifies by a low noise amplifier (LNA) for increased received signal-to-noise power ratio (SNR). The RF signal with increased SNR is downconverted (D/C) to an intermediate frequency (IF) and processed by an IF filter to clean up the signal from adjacent interference and out-of-band noise. The clean-up signal is then (a) routed to the proper downlink port by an IF analog switching circuit and upconverted (U/C) to RF, (b) combined by a multiplexer (MUX), and (c) amplified by a high-power amplifier (HPA) for downlink transmission.
\n
Figure 4.
Legacy ABPS payload architecture.
\n
As illustrated in Figure 5, there are two options for the D/C, namely Option 1 (see Figure 5(a)) is a double downconverter using two local oscilators (LOs) to downconvert RF signal to IF signal with stable and low phase noise, and Option 2 (see Figure 5(b)) is single downconverter using a LO downconverting RF signal directlty to an IF signal. Option 1 is being used in many legacy, existing, and advanced satellite payloads. Option 2 is mostly used in advanced satellite payloads.
\n
Figure 5.
Options for RF downconversion and associated LO’s phase noise.
\n
\nFigure 5(c) shows commercial-of-the-shelf (COTS) phase noise characteristics for typical LOs operating at X-band, Ku-band, and Ka-band. X-band, Ku-band, and Ka-band illustrated in this figure correspond to 7–11.2, 12–18, and 26.5–40 GHz, respectively. The main advantages of Option 2 using single downconversion are its low cost, small size, and low power consumption (also known as small SWAP-C). This option uses the smallest number of external components as compared to Option 1 using double downconversion, which is also known as super heterodyne receiver [5]. However, Option 2 suffers amplitude and phase imbalances caused by imperfect references associated with I-Q components, direct current (DC) signal due to self-mixing, and flicker noise.1 Option 1 does not suffer from these problems and offers excellent selectivity and sensitivity, that is, better rejection of adjacent interferences. Option 1’s disadvantages are the integration complexity and high SWAP-C.
\n
In satellite electronic communications, MUX is a multiplexer, which is a device that selects several (multiple) analog (or digital) input signals and outputs a single signal. Figure 6(a) describes a functional MUX (aka multiplexer) circuit. On the contrary, Figure 6(b) depicts a DEMUX (aka demultiplexer), which is an electronic device that sends a single input signal to multiple signal outputs.
\n
Figure 6.
Functional block diagrams of MUX and DEMUX.
\n
\n
\n
4.2 Existing digital bent-pipe satellite (DBPS) payload architecture
\n
\nFigure 7 presents an existing DBPS payload architecture using on-board digital channelizer. Similar to analog payload, there are two options for the RF-to-IF downconversion process. Double-downconversion process is typically used for digital bent-pipe payload architecture.
\n
Figure 7.
Existing DBPS payload architecture.
\n
\nFigure 8 depicts typical RF-to-IF (or baseband) downconversion and digitization and sampling processes for a commercial DBPS payload architecture. The RF-to-IF process shown in this figure uses Option 1, double downconversion, and the digitization and sampling process employing bandpass sampling with digital quadrature technology [6]. The RF bandwidth (BW) associated with the RF bandpass filter (BPF) is selected to match with an over channel bandwidth (e.g., a maximum of 500 MHz for Ku-band). The automated gain control (AGC) is designed to maintain a constant power over the specified channel bandwidth. There are several advantages associated with bandpass sampling with digital quadrature techniques, including (a) no phase and amplitude imbalances; (b) digital finite impulse response (FIR) filters are flexible and computational complexity with linear phase introducing a constant group delay; (c) only one A/D converter is required (less weight and power); and (d) when the sampling period is set at one-quarter of the carrier frequency, the reference in-phase and quadrature components reduce to an alternating sequence between I-channel and Q-channel [6].
\n
Figure 8.
Typical R/F downconversion and digitization processing approach.
\n
As shown in Figure 9, the key design issue associated with the digitization and sampling processing is the selection of required number of bits of the analog-to-digital (A/D) conversion to (1) achieve optimum loading factor (LF) and (2) minimize the quantization noise. The LF is defined as the root mean square (RMS) of the total input signal voltage-to-A/D converter saturation voltage ratio. The total input signal voltage includes desired signal voltage (S) plus noise voltage (N) plus interference voltage (I). Figure 10 illustrates an optimum LF as a function of number of bit of a typical A/D converter. As an example, for 4-bit, the optimum LF is about 0.4. In conjunction with LF, the number of bit should be selected to maximize the signal-to-quantization noise ratio (SQNR) using the following relationship:2\n
\n
Figure 9.
Existing digitization and sampling processing using bandpass sampling with digital quadrature technique.
\n
Figure 10.
Optimum LF as a function of number of bit of A/D converter.
As an example, when N = 4 bits, signal-to-quantization noise ratio is about 25.84 dB.
\n
The key feature of DBPS payloads is the flexibility of the digital channelizer. Current digital technologies allow for the implementation of robust and reconfigurable digital channelizer adapting to require the number of users and associated users’ data rates. A typical flexible digital channelizer using polyphase/discrete Fourier transform (DFT) technology is shown in Figure 11.
\n
Figure 11.
Typical digital channelizer using polyphase/DFT technology.
\n
As shown in Figure 11, the heart of a typical digital channelizer is a polyphase-filter network (or simply a polyphase network) and a DFT processor. A typical polyphase network with a DFT processor is described in Figure 12. The polyphase network consists of a set of NC digital filters with transfer function H0, H1..., HNc-1, which is obtained by shifting a basic low pass complex filter function along the frequency axis [7]. As an example, for a typical 500 MHz channel bandwidth, assuming for a typical user data rate of 4 MHz and a guardband of 1 MHz, digital channelizer, NC = 500/(4 + 1) = 100, that is, the number of filter is 100, and each has a total of 5 MHz bandwidth. A change in sampling frequency by a factor of NC can be introduced, thus allowing the circuit in different paths of the polyphase network to operate at lower frequency than the original sampling frequency. A practical implementation of a high-throughput low-latency polyphase channelizer can be found in [8, 9].
\n
Figure 12.
Typical Polyphase/DFT Technology.
\n
\nFigure 12 shows an example of five input signals, namely S1, S2, S3, S4, and S5, and the channelizer will select signal interest by filtering out the other signals. As an example, the signal line with the filter transfer function of H0 filters out S2, S3, S4, and S5 and sends S1 as an output signal.
\n
\n
\n
4.3 Advanced digital bent-pipe satellite using digital channelizer and beamformer (AdDBPS-DCB) payload architecture
\n
For a typical commercial HTS system architecture, it usually requires on-board multiple beam phase array (PA) antenna with associated adaptive digital beamformer network (DBF) for spot beamforming and frequency reusing of the spot beams when the beams are not located near each other. Figure 13 describes a typical AdDBPS-DCB payload architecture, where the digital channelizer is combined with a DBF to make a “digital channelizer and beamformer” (DCB) [10, 11, 12]. For this payload architecture, the key feature that differentiates this architecture with the ones discussed above is the combined digital channelizer using polyphase network/DFT processor and DBF (PolyN/DFT-DBF).
\n
Figure 13.
AdDBPS-DCB payload architecture.
\n
As pointed out in [10, 11, 12], DCB architecture shown in Figure 13 can be designed to (1) form individual beams for each active receive and transmit communication channels; (2) adaptively generate channel beam steering weights to dynamically vary the bandwidth, location, and shape of each beam based on traffic demands and the locations of other, potentially interfering beams avoiding adjacent channel interference; (3) use digital beamforming weight calibration to compensate for the temporal and thermal phase and amplitude response variations inherent in analog multibeam phased array antennas; and (4) adjust the gain of individual receive-and-transmit channel beams automatically to compensate for propagation path and analog payload response variations. In general, there are two possible DCB implementation approaches, namely DCB Approach 1 and DCB Approach 2 [13]. Figure 14 describes the DCB Approach 1 for processing the uplink signals, where the uplink signals are individually processed by the digital channelizer (i.e., PolyN/DFT processing) and DBF independently and separately. DCB Approach 1 requires a larger computational load because each DBF processes all the user link bandwidth (e.g., S1, S2, S3, S4, and S5 in Figure 12) at all times to form multiple beams.
\n
Figure 14.
DCB Approach 1: PolyN/DFT and DBFN individual processing.
\n
DCB Approach 2 is shown in Figure 15, where DCB utilizes an unified processing approach with each DBF processes only the bandwidth corresponding to a beam (S1 in Figure 12) at normal times. During anomaly operation condition (e.g., natural disaster event), when the bandwidth has to be reassigned to specific areas, the arithmetic load on DBF can be reduced by implementing multiple DBFs, with each capable of processing a bandwidth narrower than that assigned to a beam (i.e., smaller channel unit). This approach enables a reduction in wasteful arithmetic resource usage on bandwidth.
\n
Figure 15.
DCB Approach 2: Unified and combined PolyN/DFT and DBFN individual processing.
\n
If one defines the number of multipliers, D implemented in each Tx/Rx DBF as C/fop, where C is the computational load of a DBF (multiplications/sec), and fop is the operation frequency of the multiplier. Let us compare D calculations between DCB Approach 1 and DCB Approach 2. Let us assume the following parameters: n is the number of array elements, m is the number of beams, an userlink processing bandwidth of 28 MHz, 5 frequency repetitions of the userlink, and an operating frequency of multiplier of 256 MHz. Using these values, D for the DBF/channelizer of the DCB Approach 1 configuration becomes [13]:
The latter calculation assumes an ideal case in which DBF network (DFBN) processing is performed on a channel-by-channel basis. The complexity of DCB Approach 2 configuration is 10 times less complex than DCB Approach 1.
\n
As pointed out in [12], the DBFN when coupled with a digital channelizer (aka DCB) offered more capabilities with many advantages. Nguyen et al. [14] developed a computer simulation model of a typical DBFN in MATLAB and presented simulation results for X, Ku, and Ka BFNs using 60-element, 104-element, and 149-element, respectively. Figure 16 is an extracted Ka-band BFN result showing the achievable antenna gain of 45.5 dB at 3-dB beamwidth of 0.9°. For practical applications, the DBFN will shape the beam size depending on the coverage area and desired number of beams. Nguyen et al. [14] pointed out that for 2.5° coverage area and the desired number of beams of 7, the minimum 3-dB beamwidth of 1.1° is required. Nguyen et al. [14] also pointed out that DCB can provide a significant increase in frequency reuse, where the frequency reuse is defined as the number of times a satellite can reuse the same spectrum and frequencies. High frequency reuse factor can cause potential cochannel interference (CCI) that results in a decrease in carrier-to-interference power ratio [aka (C/I) CCI]. As pointed out in [14], for dynamic allocation using real-time allocation of beams so that the coverage radius of a cell is equal to the satellite pointing error, assuming satellite pointing error of 0.02 degree pointing error, the (C/I)CCI is about 25 dB for frequency reuse factor 40 [14].
\n
Figure 16.
Antenna beamwidth and gain of a notional Ka-band DBFN with 12-bit quantization [14].
\nFigure 17 depicts a potential future AR-OBPS payload architecture [10]. The payload includes (1) a typically set of digitized analog multiple beam antenna (MBA) input signals, digitally frequency division demultiplex each input signal to produce single carrier per channel (SCPC) signal data and demodulate and decode individual traffic channels to recover the original information bits transmitted on the uplink; (2) a set of digitized analog multibeam phase array antenna (MB-PAa) input signals, digitally frequency division demultiplex each input signal to produce SCPC signal data and demodulate and decode individual traffic channels to recover the original information bits transmitted on the uplink; and (3) fast packet switches are typically employed at the AR-OBPS payload’s core to realize statistical multiplexing gains by efficiently packing and moving data through the switch and onto the downlink in bursty uplink transmission applications. Moreover, the digital bandwidth (in Hz) through the AR-OBPS switch is at least 25 times less3 than that supported by an equivalent (pre-demodulation) digital baseband switch at the center of a DC- or DCB-based system. AR-OBPS payload can also support digital beamforming, following the frequency division demultiplexing operation, if a phased array is employed in place of the analog MBA. On the secondary (output) side of the switch, each user’s binary information is channel encoded and modulated onto a carrier. The modulated carrier data thus produced are multiplexed, digital-to-analog converted, and passed through an analog reconstruction filter to generate output signals for the transmit portion of the communication payload. The channel codes and modulations employed on the uplink (input) communication channels clearly do not need to be the same as the channel codes and modulations used on the transmitted downlink channels. Hence, an AR-OBPS payload can serve as a “translator” facilitating single-hop communications between terminals employing different link protocols. However, if either the digital multichannel demultiplexer (DMCD), demodulator, decoder, or digital multichannel multiplexer (DMCM) encoder modulator, multiplexer (MCEM2) functions are implemented in ASICs to minimize size-weight-and-power (SWaP), then the AR-OBPS system becomes somewhat inflexible, unable to support either uplink or downlink terminals, respectively, using communication protocols differing from those for which the AR-OBPS was specifically designed. For this reason, AR-OBPS systems are typically employed in support of “private networks” in which the communication satellite service provider only accommodates terminals designed to work on the provider’s network. Iridium and Spaceway are two examples of commercial AR-OBPS-based communication satellite systems.
\n
Figure 17.
AR-OBPS payload architecture.
\n
\n
\n
\n
5. Satellite system architectures for 5G cellular phone applications
\n
Sections 5.1 and 5.2 present a notional satellite system architecture using AdBPS-DCBS satellite payload and AR-OBPS satellite system architecture for 5G cellular phone applications, respectively.
\n
\n
5.1 AdBPS-DCBS satellite system architecture for 5G applications
\n
AdBPS-DCBS satellite payload can be used to support 5G users. There are potentially two satellite system architecture options for using AdBPS-DCBS satellite payload to support 5G mobile user equipment (aka 5G-UE), namely AdBPS-DCBS Option 1 and AdBPS-DCBS Option 2. For AdBPS-DCBS Option 1, the AdBPS-DCBS satellite provides communication services directly to 5G-UEs. While in AdBPS-DCBS Option 2, the satellite provides services to 5G-UEs through the 5G relay nodes (RNs). Figure 18 illustrates the AdBPS-DCBS satellite system architecture for (a) AdBPS-DCBS Option 1 and (b) AdBPS-DCBS Option 2 [15].
\n
Figure 18.
AdDBPS-DCB satellite system architectures for supporting 5G users.
\n
\nFigure 18(a) shows that the AdBPS-DCBS satellite requires new radio (NR) interfaces between (1) AdBPS-DCBS satellite and terrestrial gateway (GW) and (2) AdBPS-DCBS satellite and 5G-UEs. In addition, it is also required a 5G narrow-band (gNB) processing station to process the 5G signals from the next generation core (NGC) network before passing the 5G data to public data network.
\n
\n
\n
5.2 AR-OBPS satellite system architecture for 5G applications
\n
Similar to AdBPS-DCBS satellite payload, AR-OBPS satellite payload can also be used to support 5G users. There are also two satellite system architecture options for using AR-OBPS payload to support 5G mobile user equipment, namely AR-OBPS Option 1 and AR-OBPS Option 2. For AR-OBPS Option 1, the AR-OBPS satellite provides communication services directly to 5G-UEs. For AR-OBPS Option 2, the satellite provides services to 5G-UEs through the 5G RNs. Figure 19 describes these two AR-OBPS architecture options, namely (a) for AR-OBPS Option 1 and (b) for AR-OBPS Option 2. For these two system architecture options, the gNB processing is now incorporated into the AR-OBPS satellite payload and no longer required for the ground system. The GW now can pass the 5G data directly to the NGC. The decoding-demodulation and encoding-modulation processing on-board of the satellite will be designed to align with the 5G waveform specifications, including 5G modulation and coding schemes.
\n
Figure 19.
AR-OBPS satellite system architectures for supporting 5G users.
\n
\nFigure 19(a) shows that the AR-OBPS satellite also requires NR interfaces between (1) AR-OBPS satellite and GW and (2) AR-OBPS satellite and 5G-UEs. Similar to AdBPS-DCBS satellite system architecture options, the NR interfaces between the AR-OBPS satellite and 5G-UEs are new. Since the gNB processing is now placed at AR-OBPS satellite payload, the NR interfaces between AR-OBPS satellite and 5G-UEs are not the same as the AdBPS-DCBS satellite and 5G-UEs. To show the differences between the two, Figures 19(a) and (b) use Sat-NG-C and Sat-NG-U to indicate the new radio interface between (1) terrestrial GW-NGC-and-AR-OBPS satellite and (2) AR-OBPS satellite-and-terrestrial GW-NGC, respectively.
\n
\n
\n
\n
6. Conclusion
\n
This chapter uses a top-down approach for providing an overview of legacy, existing, and future advanced satellite payload architectures for future wireless communication applications. The chapter focuses on the commercial satellite technologies based on the research results presented in [1, 2]. Section 2 provides the comparison results performed by Inmarsat describing the technical characteristics and associated advantages and disadvantages between commercial HTS and typical satellite systems currently available in commercial satellite market. In Section 3, two most commonly satellite network topologies used by existing commercial satellite networks are presented, and the concept of satellite uplink and downlink associated with star satellite network and mesh satellite network is discussed. The satellite network topologies presented lead to Section 4, where four satellite payload architectures are discussed. The legacy analog ABPS payload architecture is shown to be more appropriate for star satellite network than mesh network. Existing digital DBPS and AdDBPS-DCB payload architectures are designed for supporting mesh satellite network with large number of mobile users. Future advanced digital satellite payload architecture, namely AdDBPS-DCB, is also presented in this section. With decoding-demodulating and encoding-modulating processing on-board of the satellite, AR-OBPS allows for packet switching on-board and higher quality of service (QOS) than existing DBPS and AdDBPS-DCB at the expense of higher SWAP-Cost (SWAP-C). Section 4 of the chapter discusses the applications of AdBPS-DCBS and AR-OBPS payloads for supporting 5G users. Four satellite system architecture options are presented for supporting the future 5G users.
\n
\n
Conflict of interest
The preparation of this chapter was not funded by Gulfstream, and it was done by the author using his own time and resources; thus, it does not represent the Gulfstream’s view on the results presented in this chapter.
\n
Notes/Thanks/Other declarations
\n
The author wishes to thank his wife, Annie Luu-Nguyen, for her immense patience and support.
\n
\n',keywords:"high-throughput satellite, analog bent-pipe satellite, digital bent-pipe satellite, digital channelizer and beamformer, advanced regenerative on-board processing satellite, cellular phone",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/72839.pdf",chapterXML:"https://mts.intechopen.com/source/xml/72839.xml",downloadPdfUrl:"/chapter/pdf-download/72839",previewPdfUrl:"/chapter/pdf-preview/72839",totalDownloads:86,totalViews:0,totalCrossrefCites:0,dateSubmitted:"December 7th 2019",dateReviewed:"June 19th 2020",datePrePublished:"July 17th 2020",datePublished:null,dateFinished:null,readingETA:"0",abstract:"This chapter presents an overview of legacy, existing, and future advanced satellite systems for future wireless communications. The overview uses top-down approach, starting with a comparison between a typical commercial regular satellite system and a high-throughput satellite (HTS) system, following by a discussion on commonly used satellite network topologies. A discussion on the design of satellite payload architectures supporting both typical regular satellite and HTS with associated network topologies will be presented. Four satellite payload architectures will be discussed, including legacy analog bent-pipe satellite (ABPS); existing digital bent-pipe satellite (DBPS) and advanced digital bent-pipe satellite using digital channelizer and beamformer (AdDBPS-DCB); and future advanced regenerative on-board processing satellite (AR-OBPS) payload architectures. Additionally, various satellite system architectures using AdBP-DCBS and AR-OBPS payloads for the fifth-generation (5G) cellular phone applications will also be presented.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/72839",risUrl:"/chapter/ris/72839",signatures:"John Nguyen",book:{id:"7030",title:"Satellite Systems - Design, Modeling, Simulation and Analysis",subtitle:null,fullTitle:"Satellite Systems - Design, Modeling, Simulation and Analysis",slug:null,publishedDate:null,bookSignature:"Dr. Tien Manh Nguyen",coverURL:"https://cdn.intechopen.com/books/images_new/7030.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"210657",title:"Dr.",name:"Tien",middleName:"Manh",surname:"Nguyen",slug:"tien-nguyen",fullName:"Tien Nguyen"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Background and introduction",level:"1"},{id:"sec_2",title:"2. Typical commercial satellites and HTS comparison",level:"1"},{id:"sec_3",title:"3. Typical commercial satellite network topologies",level:"1"},{id:"sec_3_2",title:"3.1 Typical “star” satellite network",level:"2"},{id:"sec_4_2",title:"3.2 Typical “mesh” satellite network",level:"2"},{id:"sec_6",title:"4. Legacy, existing, and advanced satellite payload architectures",level:"1"},{id:"sec_6_2",title:"4.1 Legacy analog bent-pipe satellite (ABPS) payload architecture",level:"2"},{id:"sec_7_2",title:"4.2 Existing digital bent-pipe satellite (DBPS) payload architecture",level:"2"},{id:"sec_8_2",title:"4.3 Advanced digital bent-pipe satellite using digital channelizer and beamformer (AdDBPS-DCB) payload architecture",level:"2"},{id:"sec_9_2",title:"4.4 Future advanced regenerative on-board processing satellite (AR-OBPS) payload architecture",level:"2"},{id:"sec_11",title:"5. Satellite system architectures for 5G cellular phone applications",level:"1"},{id:"sec_11_2",title:"5.1 AdBPS-DCBS satellite system architecture for 5G applications",level:"2"},{id:"sec_12_2",title:"5.2 AR-OBPS satellite system architecture for 5G applications",level:"2"},{id:"sec_14",title:"6. Conclusion",level:"1"},{id:"sec_18",title:"Conflict of interest",level:"1"},{id:"sec_15",title:"Notes/Thanks/Other declarations",level:"1"}],chapterReferences:[{id:"B1",body:'\nMagnuson S. Commercial space—Not military—Driving satellite innovation. National Defense Magazine. 2018. Available from: https://www.nationaldefensemagazine.org/articles/2018/4/17/commercial-space-not-military-driving-satellite-innovation\n\n'},{id:"B2",body:'\nRevillon P. Fundamentals and Dynamics of the Satellite Communications Business. Presented at the Euroconsult for Inmarsat Capital Markets Day in 2016. Available from: www.euroconsult-ec.com\n\n'},{id:"B3",body:'\nStar Network. Wikipedia. Available from: https://en.wikipedia.org/wiki/Star_network\n\n'},{id:"B4",body:'\nMesh Network. Wikipedia. Available from: https://en.wikipedia.org/wiki/Mesh_networking\n\n'},{id:"B5",body:'\nSuperheterodyne Receiver. Wikipedia. Available from: https://en.wikipedia.org/wiki/ Superheterodyne_receiver\n\n'},{id:"B6",body:'\nSadr R, Shahshahani M. On Sampling Band-Pass Signals, TDA Progress Report 42-96. Pasadena, California: NASA-Jet Propulsion Laboratory; 1988\n'},{id:"B7",body:'\nvan der Veldt K, van Nieuwpoort R, Varbanescu AL, Jesshope C. A polyphase filter for GPUs and multi-core processors. In: Proceedings of the Workshop on High-Performance Computing for Astronomy; Delft, Netherlands; 18 June, 2012. pp. 33-40\n'},{id:"B8",body:'\nKim SC, Bhattacharyya SS. Implementation of a high-throughput low-latency polyphase channelizer on GPUs. In: EURASIP Journal on Advances in Signal Processing; 2014. Available from: http://asp.eurasipjournals.com/content/2014/1/141\n\n'},{id:"B9",body:'\nEjima F, Akita M, Fujimura A. Digital Channelizer for High Throughput Satellite Communications, Technical Paper. Mitsubishi Electric Advance (Technical Journal published by Mitsubishi); 2014. pp. 7-10\n'},{id:"B10",body:'\nButash TC, Marshall JR. Leveraging digital on-board processing to increase communications satellite flexibility and effective capacity. In: 28th AIAA ICSSC. AIAA 2010-8715; Anaheim, CA; 30 August–2 September 2010\n'},{id:"B11",body:'\nSichi SF, Ziegler HE. Beamforming architectures for advanced MSS network deployment. In: 29th AIAA ICSSC; AIAA 2011-8021; Nara, Japan; 28 November–01 December, 2011\n'},{id:"B12",body:'\nFreedman JB, Marshack DS, et al. Advantages and capabilities of a beamforming satellite with a space-based digital processor. In: 32nd AIAA International Communications Satellite Systems Conference (ICSSC); AIAA 2014-4321; San Diego, CA; 4-7 August 2014\n'},{id:"B13",body:'\nKomiyama N, Miura A, Orikasa T, Fujino Y. Development of resource allocation re-construction technology (digital beam former and digital channelizer). Journal of the National Institute of Information and Communications Technology. 2015. pp. 151-163\n'},{id:"B14",body:'\nNguyen TM, Guillen A, Matsunaga S. Practical achievable capacity for advanced SATCOM on-the-move. In: 2016 IEEE MILCOM Conference Proceedings; 2016\n'},{id:"B15",body:'\nGuidotti A, Vanelli-Coralli A, Conti M, Andrenacci S, Chatzinotas S, Maturo N, et al. Architectures and key technical challenges for 5G systems incorporating satellites. IEEE Transactions on Vehicular Technology. 2019. pp. 2624-2639\n'}],footnotes:[{id:"fn1",explanation:"Flicker noise is a type of electronic noise with a 1/frequency power spectral density."},{id:"fn2",explanation:"Quantization (signal processing). Available from: https://en.wikipedia.org/wiki/Quantization_(signal_processing)."},{id:"fn3",explanation:"Assumes 1 bps/Hz modulation efficiency, 10 bit signal data quantization, and 2.5× practical Nyquist sampling rate."}],contributors:[{corresp:"yes",contributorFullName:"John Nguyen",address:"johndncva@gmail.com",affiliation:'
JohnDTN Consulting Services, Huntington Beach, California, USA
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Integrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
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Openness - We communicate honestly and transparently. We are open to constructive criticism and committed to learning from it.
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Disruptiveness - We are eager for discovery, for new ideas and for progression. We approach our work with creativity and determination, with a clear vision that drives us forward. We look beyond today and strive for a better tomorrow.
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What makes IntechOpen a great place to work?
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IntechOpen is a dynamic, vibrant company, where exceptional people are achieving great things. We offer a creative, dedicated, committed, and passionate environment but never lose sight of the fact that science and discovery is exciting and rewarding. We constantly strive to ensure that members of our community can work, travel, meet world-renowned researchers and grow their own career and develop their own experiences.
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If this sounds like a place that you would like to work, whether you are at the beginning of your career or are an experienced professional, we invite you to drop us a line and tell us why you could be the right person for IntechOpen.
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