Summary of QSAR Models for Predicting LogPS (Lanevskij et al., 2008)
In this chapter, we briefly summarize the concept of analytical methodologies used for detecting, measuring, and/or monitoring in public health research. Additionally, this chapter describes
Recently, adopted technologies to cope with this type of scientific demand in terms of drug development and testing are the applications of
Figure 2 shows a simple ADME decision-making flow sheet which will incorporate predictors for volume of distribution, oral bioavailability, half-life (t1/2), distribution\protein binding module including percentage plasma protein binding values (
Figures 2a and 2b outline the parameters in the prediction of a safe drug given in acceptable dose, which it is ultimately hoped will be reliably obtainable from molecular structure and appropriate descriptors using a suite of predictive models. This expression had earlier been made clear by Japertas and coworkers (2011).
In figure 2c (Waterbeemd & Gifford, 2003), the form
It should be noted that all three activities shown in figure 3 can even be carried out by separate companies of research units or even researchers in health departments or for health interest. A good example is the Medical Research Council (MRC) of South Africa which supports unit health research projects towards a nationally planned and prioritized health sectors. Furthermore, the wide introduction of
2. Analytical methods in health research
As analytical methods become increasingly sophisticated and capable for the detection of each component in a sample (Barnes & Dourson, 1988), including biological systems (Kote-Jarai et al. 2011), it is critical to separate and quantify them. Methods such as mass spectrometry (MS) and high performance chromatography (HPLC) routinely ensure these in many laboratories around the world (Thorp et al., 2011). HPLC instrumentations provide crucial analytical data (Beitler, 1995) used to calculate or predict drug’s affinity constants (
In this process, as tons of data are being produced with the analytical chemists struggling to make sense out of the bunch, the health researcher and health practitioners are faced with a constant task to make better and faster decisions in the area of disease treatment and prevention based on laboratory results. In the midst of all this, there is the requirement not only to produce efficient drugs, in enough quantities, to cure diseases but their development at the pace at which pandemics are spreading around the globe is also required, for example, cancer and HIV & AIDS.
As far as the identification of data needs is crucial in clinical laboratories, the quest for methods to determine biomarkers of exposure and effect of diseases in the public health is also growing fast. Hence, analysis of metabolites of drugs in humans or animals can provide a biomarker of exposure that is sensitive to low levels of exposure and correlates well with exposure concentrations. Methods for determining biomarkers of exposure in humans are needed to determine background levels in the population and levels at which biological effects occur. For example, Abdel-Rahman et al. (1980a & 1980b) developed a method to quantitatively and qualitatively measure the metabolites of chlorine dioxide (e.g., ClO2-, and ClO-) in biological fluids. These biomarkers may be used to indirectly measure chlorine dioxide exposure.
In the absence of sensitive and reliable methods for determining diseases vector-borne metabolites and biomarkers of exposure, mechanistic models of tissue distribution of drug compounds have been used (Rowley et al., 1997) to assess levels at which biological effects occur in the population and mitigate disease occurrence. Poulin et al. (Poulin & Theil, 2002; Poulin & Krishna, 1995) developed tissue composition-based equations for calculating tissue-plasma partition coefficients (Pt:p).
The following expressions are used (equations 1 and 2) (Yamashita & Hashida, 2004):
Po:w is the n-octanol:buffer partition coefficient of non-ionized species at pH 7.4.
D*vo:w is the olive oil:buffer partition coefficient of both the nonionized and ionized species at pH 7.4, V is the fractional tissue volume content of neutral lipids (
These equations are based on the assumption that each tissue and plasma is a mixture of lipids, water and plasma proteins in which the drug can be homogeneously distributed.
The first term of these equations is based on the drug Lipophilicity-hydrophilicity balance of tissues and plasma due to their lipid and water contents, while the second term of the equation considers the binding to common proteins present in plasma and plasma interstitial space.
3. Use of
In-silico techniques and chemical informatics in health research
Even though disease mapping has been done for over a hundred years, historically, the focus in health research has been on person and conventional medicinal chemistry targeting specific disease treatment with little regard for the implications of
In the last decade, a wide variety of descriptors used in QSAR studies have been developed (Khan et al., 2009; Miners et al., 2006). A subset of these descriptors is potentially useful for predicting ADME properties. Many QSAR studies on BBB permeation of drugs have been published recently. In the big junk of these works (Wichmann et al., 2007; Zhao et al., 2007; Cuadrado et al., 2007; Katritzky et al., 2006; Garg & Verma, 2006; Hemmateenejad et al., 2006; Narayanan & Gunturi, 2005) experimental data are represented as log
|Abraham and coworkers10,24||Solvation parameters (A, B, E, S, Vx)||18||0.95||0.48|
|Bodor and Buchwald11||Log
|Liu et al.5||Log
|Luco and Marchevsky25
(review of earlier studies)
These illustrations show how
From various literature sources (Bhal, 2007; Sazonovas et al., 2010), it is reported that ADME and toxicity prediction models can be a valuable part of many different research workflows, including virtual screening, metabolite identification, impurity analysis and chemical safety, reliability index (
The development of
For a more robust process, calculated quantitative parameters will provide further information though slightly different from the core predictive pharmacokinetic data. These parameters show great inter relation. Such parameters include the drug’s affinity constants (
Where [LA] is the concentration of ligand bound to albumin, [L] is that of free ligand, and [A] is the concentration of free albumin which, estimated at ~ 0.6
To illustrate the performance of
In order to understand the behavior of drug compounds in the real world, Bhal et al. (2012) has used ACD/Predictors such as log
For ionizable solutes, the compounds may exist as a variety of different species in each phase at a given pH. D, typically used in the logarithmic for (lo
Bhal (2012) used methylamine to illustrate the difference between these two descriptors as follows: MeNH3+ MeNH2 + H+, meaning that,and/or . So, keeping the definition of D above in mind, we can write the expression of D as:
To accurately predict a compound’s lipophilicity based on predicted molecular physical properties, it was imperative that the author applied the correct descriptor in an appropriate manner. In this context, log
The pH dependence of log
Looking at the plot in figure 8, and according to Bhal el al. (2012), we can confirm that ionization of the compound greatly affects octanol-water partitioning and that lipophilicity cannot be simplified to a constant. This is very so as lipophilicity of the compound is low below pH 12 when the majority of the compound exists in an ionized form. This would definitively be contradictory, of course, if log
The author concludes that the negative values of log
Figure 9 shows a schematic representation of the changing pH environments that an orally administered compound is likely to encounter in the gastrointestinal (GI) tract. From figure 9, we can observe that there is, thus, no constant pH in the body and it is therefore essential that we consider an appropriate pH when predicting the
4. Implications of analytical methods and
in-silico techniques in public health
The outcomes from a global network on the development of standardized analytical methods for the public and environmental health directly impacts on the quality of health of mankind worldwide. High quality data necessitates the development of harmonized study approaches and adequate reporting of data (Bouwmeester et al., 2011).
Priority public health scale can only be based on well-characterized dose-response relations derived from a systematic study of the bio-kinetics and bio-interactions of drugs or drug-like molecules at both organism and (sub)-cellular levels using validated analytical methods and pharmacokinetic studies. The ADME (absorption, distribution, metabolism and excretion) and toxicity effects is crucial to declare a particular molecule safe for the treatment of a particular disease and often clinical trials to arrive at a conclusive release of a new drug very costly, sometimes in the range of millions of dollars covering the cost of fundamental research through clinical trials or testing to manufacturing. Multiple content databases, data mining and predictive modeling algorithms, visualization tools, and high-throughput data-analysis solutions are being integrated to form systems-ADME/Tox (Ekins et al., 2005). More so, Ekins and co-authors (2005) reported that the functional interpretation and relevance of complex multidimensional data to the phenotype observed in humans is the focus of current research in toxicology.
In fact, increased effort is needed to develop and validate analytical methods to determine ADMET effects in complex matrices such as the human body. This implies the use of validated analytical methods and
Metabolomics, metabonomics, proteomics, pharmacogenomics and toxicogenomics, are groups of latest experimental approaches that are combined with high-throughput molecular screening of targets to provide a view of the complete biological system that is modulated by a compound with direct or indirect implications of analytical methods and
5. General conclusion
Driven by the changes in the working paradigm in the pharmaceutical and biotechnology, and now in environmentally health-related research,
Conclusively, ADMET data is tackled in three ways, namely: first, a variety of
Since most models are rule-based and may use descriptors that are not easily understood by the chemist or not easily translated into better molecular structures, it is important to constantly train models of datasets. A combo approach, combining first generation (basic predictive descriptors) and second generation (meta-models) computational ADMET technologies would be the best way to go.
To then get value for your money, it is clearly demonstrated that ADME predictive tools is imperative, nowadays, in the health research programs in order to cut costs and propose reliable lead drug-like compounds. It is though highly desirable and recommendable to add in-house data in the prediction models whenever available. Sensitive and reliable high throughput instrumentations are a prerequisite in generating in-house analytical data necessary for efficient and useful predictive processes. Training data sets in models would be an added advantage for a wide range of investigations in health related research.
The author would like to acknowledge the application scientists at the Advanced Chemistry Development Inc., (Toronto, Canada), in particular, Dr Sanji Bhal for providing some of the illustrative predictive examples. Many thanks go to the reviewers for their helpful suggestions and revision of this chapter. Finally, the authors are grateful to Dr GPP Kamatou of the Department of Pharmaceutical Sciences of the Tshwane University of Technology, Pretoria, South Africa, for reading through the proof of the chapter and hereby acknowledge his positive criticism.
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