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The 2017 Guidance by U.S. Food and Drug Administration (FDA) has recommended the criteria to qualify for a Biopharmaceutical Classification System (BCS)-based biowaiver that includes high solubility of the drug across the physiological pH range as well as the formulation considerations, e.g., being qualitatively the same and quantitatively very similar to the reference product. These were ratified by the International Council for Harmonization (ICH) in 2018. The FDA has used the similar verbiage when referring to the BCS-based biowaiver option for BCS class III drugs (highly soluble but poorly permeable). However, establishing in vitro-in vivo correlations (IVIVC) using conventional mass balance deconvolution approaches, which assumes a single absorption compartment, is not likely for very rapidly dissolving dosage forms containing BCS III drugs. Unlike conventional mass balance deconvolution techniques, physiologically based pharmacokinetic models are able to disentangle different processes contributing to the input function, e.g., dissolution, gastrointestinal transit, and permeation and to establish IVIVC using variants of the compartmental absorption and transit model, supporting biowaiver for formulations containing BCS III drugs. However, there are knowledge gaps that need to be filled. This review provides a systematic assessment of the advancements in applications of physiologically based pharmacokinetic (PBPK) models for IVIVC and biowaiver for such cases with the aim of identifying the most important gaps and hurdles. It concludes by calling for research efforts on the impact of excipients on dissolution and permeation, alongside the development of PBPK modeling to link these in vitro characteristics to in vivo bioequivalence outcomes through simulations of virtual clinical studies., (© 2021 John Wiley & Sons Ltd.)

Solubility and permeability are recognized as key parameters governing drug intestinal absorption and represent the basis for biopharmaceutics drug classification. The Biopharmaceutics Classification System (BCS) is widely accepted and adopted by regulatory agencies. However, currently established low/high permeability and solubility boundaries are the subject of the ongoing scientific discussion. The aim of the present study was to apply data mining analysis on the selected drugs data set in order to develop a human permeability predictive model based on selected molecular descriptors, and to perform data clustering and classification to identify drug subclasses with respect to dose/solubility ratio (D/S) and effective permeability (P eff ). The P eff values predicted for 30 model drugs for which experimental human permeability data are not available were in good agreement with the reported fraction of drug absorbed. The results of clustering and classification analysis indicate the predominant influence of P eff over D/S. Two P eff cut-off values (1 × 10 -4 and 2.7 × 10 -4  cm/s) have been identified indicating the existence of an intermediate group of drugs with moderate permeability. Advanced computational analysis employed in the present study enabled the recognition of complex relationships and patterns within physicochemical and biopharmaceutical properties associated with drug bioperformance., (© 2019 John Wiley & Sons, Ltd.)

The influence of pH on human intestinal absorption is frequently not considered in early drug discovery studies in the modelling and subsequent prediction of intestinal absorption for drug candidates. To bridge this gap, in this study, experimental membrane permeability data were measured for current and former drug substances with a parallel artificial membrane permeability assay (PAMPA) at different pH values (3, 5, 7.4 and 9). The presented data are in good agreement with human intestinal absorption, showing a clear influence of pH on the efficiency of intestinal absorption. For the measured data, simple and general quantitative structure-activity relationships (QSARs) were developed for each pH that makes it possible to predict the pH profiles for passive membrane permeability (i.e., a pH-permeability profile), and these predictions coincide well with the experimental data. QSARs are also proposed for the data series of highest and intrinsic membrane permeability. The molecular descriptors in the models were analysed and mechanistically related to the interaction pattern of permeability in membranes. In addition to the regression models, classification models are also proposed. All models were successfully validated and blind tested with external data. The models are available in the QsarDB repository (http://dx.doi.org/10.15152/QDB.203)., (Copyright © 2018 Elsevier B.V. All rights reserved.)

A semi-physiological two compartment pharmacokinetic model with two active metabolites (primary (PM) and secondary metabolites (SM)) with saturable and non-saturable pre-systemic efflux transporter, intestinal and hepatic metabolism has been developed. The aim of this work is to explore in several scenarios which analyte (parent drug or any of the metabolites) is the most sensitive to changes in drug product performance (i.e. differences in in vivo dissolution) and to make recommendations based on the simulations outcome. A total of 128 scenarios (2 Biopharmaceutics Classification System (BCS) drug types, 2 levels of K M Pgp , in 4 metabolic scenarios at 2 dose levels in 4 quality levels of the drug product) were simulated for BCS class II and IV drugs. Monte Carlo simulations of all bioequivalence studies were performed in NONMEM 7.3. Results showed the parent drug (PD) was the most sensitive analyte for bioequivalence trials in all the studied scenarios. PM and SM revealed less or the same sensitivity to detect differences in pharmaceutical quality as the PD. Another relevant result is that mean point estimate of C max and AUC methodology from Monte Carlo simulations allows to select more accurately the most sensitive analyte compared to the criterion on the percentage of failed or successful BE studies, even for metabolites which frequently show greater variability than PD., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The aim of this work is to develop a gastrointestinal (GI) drug absorption model based on a reaction limited model of dissolution and consider its impact on the biopharmaceutic classification of drugs. Estimates for the fraction of dose absorbed as a function of dose, solubility, reaction/dissolution rate constant and the stoichiometry of drug-GI fluids reaction/dissolution were derived by numerical solution of the model equations. The undissolved drug dose and the reaction/dissolution rate constant drive the dissolution rate and determine the extent of absorption when high-constant drug permeability throughout the gastrointestinal tract is assumed. Dose is an important element of drug-GI fluids reaction/dissolution while solubility exclusively acts as an upper limit for drug concentrations in the lumen. The 3D plots of fraction of dose absorbed as a function of dose and reaction/dissolution rate constant for highly soluble and low soluble drugs for different "stoichiometries" (0.7, 1.0, 2.0) of the drug-reaction/dissolution with the GI fluids revealed that high extent of absorption was found assuming high drug- reaction/dissolution rate constant and high drug solubility. The model equations were used to simulate in vivo supersaturation and precipitation phenomena. The model developed provides the theoretical basis for the interpretation of the extent of drug's absorption on the basis of the parameters associated with the drug-GI fluids reaction/dissolution. A new paradigm emerges for the biopharmaceutic classification of drugs, namely, a model independent biopharmaceutic classification scheme of four drug categories based on either the fulfillment or not of the current dissolution criteria and the high or low % drug metabolism., (Copyright © 2018. Published by Elsevier B.V.)

A great number of new drug candidates identified from the discovery pipeline are poorly water soluble, which is a drawback to bring such candidates into the pharmaceutical market. Formulating these compounds as self-emulsifying/microemulsifying/ nanoemulsifying drug delivery systems (SEDDS/SMEDDS/SNEDDS) within lipid based formulations is of growing interest. Some of the recent studies have resulted in commercial products that provided improved bioavailability and dissolution due to the better dispersion properties of SEDDS/SMEDDS/SNEDDS. An ongoing challenge that the pharmaceutical industry is facing is identifying in vitro tests that are needed in order to predict the behavior of dosage forms in the GI tract. The goal of the current review is to present the various levels of in vitro-in vivo correlations (IVIVCs) and to provide tools on the utilization of the IVIVCs in product development and optimization of SEDDS/SMEDDS/SNEDDS., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

The approach for predicting the degree of drug-induced liver injury (DILI) risk was investigated quantitatively in a modified multiparametric assay using HepaRG cells. Thirty-eight drugs were classified by DILI risk into five categories based on drug labels approved by the Food and Drug Administration (FDA) as follows: withdrawn (WDN), boxed warning (BW), warnings and precautions (WP), adverse reactions (AR), and no match (NM). Also, WP was classified into two categories: high and low concern. Differentiated HepaRG cells were treated with drugs for 24 h. The maximum concentration was set at 100-fold the therapeutic maximum plasma concentration (C max ). After treatment with drugs, the cell viability, glutathione content, caspase 3/7 activity, lactate dehydrogenase leakage and albumin secretion were measured. As modified cut-off values of each parameter, the TC 50 (toxic concentration that decreased the response by 50%) and EC 200 (effective concentration giving a response equal to 200% of controls) were calculated. In addition, the toxicity score (total sum score of the cytotoxic level of each parameter) was calculated. This modified multiparametric assay showed an 87% sensitivity and 87% specificity for predicting the DILI risk. The toxicity score showed a good predictive performance for WDN, BW and WP (high concern) categories [cut-off: score ≥ 1; area under a receiver operating characteristic curve (ROC-AUC): 0.88], and for WDN and BW categories (cut-off: score ≥ 3; ROC-AUC: 0.88). This study newly indicated that the degree of DILI risk might be predictable quantitatively by assessing the toxicity score in the modified multiparametric assay using HepaRG cells. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

This review aims to present and compare various Torsade de pointes propensity classification schemes of drugs that are publicly available from many scientific sources. We have also tracked and listed the compounds that were differently categorized. Additionally, we would like to draw attention to the need for establishing a general, standardized classification of a drug's proarrhythmic propensity. This is especially important in the current drug development process because of the changing paradigm of drug cardiac safety assessment., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Three Physiologically Based Pharmacokinetic software packages (GI-Sim, Simcyp® Simulator, and GastroPlus™) were evaluated as part of the Innovative Medicine Initiative Oral Biopharmaceutics Tools project (OrBiTo) during a blinded "bottom-up" anticipation of human pharmacokinetics. After data analysis of the predicted vs. measured pharmacokinetics parameters, it was found that oral bioavailability (F oral ) was underpredicted for compounds with low permeability, suggesting improper estimates of intestinal surface area, colonic absorption and/or lack of intestinal transporter information. F oral was also underpredicted for acidic compounds, suggesting overestimation of impact of ionisation on permeation, lack of information on intestinal transporters, or underestimation of solubilisation of weak acids due to less than optimal intestinal model pH settings or underestimation of bile micelle contribution. F oral was overpredicted for weak bases, suggesting inadequate models for precipitation or lack of in vitro precipitation information to build informed models. Relative bioavailability was underpredicted for both high logP compounds as well as poorly water-soluble compounds, suggesting inadequate models for solubility/dissolution, underperforming bile enhancement models and/or lack of biorelevant solubility measurements. These results indicate areas for improvement in model software, modelling approaches, and generation of applicable input data. However, caution is required when interpreting the impact of drug-specific properties in this exercise, as the availability of input parameters was heterogeneous and highly variable, and the modellers generally used the data "as is" in this blinded bottom-up prediction approach., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Purpose: To assess the utility of Extended Clearance Classification System (ECCS) in understanding absorption, distribution, metabolism, and elimination (ADME) attributes and enabling victim drug-drug interaction (DDI) predictions., Methods: A database of 368 drugs with relevant ADME parameters, main metabolizing enzymes, uptake transporters, efflux transporters, and highest change in exposure (%AUC) in presence of inhibitors was developed using published literature. Drugs were characterized according to ECCS using ionization, molecular weight and estimated permeability., Results: Analyses suggested that ECCS class 1A drugs are well absorbed and systemic clearance is determined by metabolism mediated by CYP2C, esterases, and UGTs. For class 1B drugs, oral absorption is high and the predominant clearance mechanism is hepatic uptake mediated by OATP transporters. High permeability neutral/basic drugs (class 2) showed high oral absorption, with metabolism mediated generally by CYP3A, CYP2D6 and UGTs as the predominant clearance mechanism. Class 3A/4 drugs showed moderate absorption with dominant renal clearance involving OAT/OCT2 transporters. Class 3B drugs showed low to moderate absorption with hepatic uptake (OATPs) and/or renal clearance as primary clearance mechanisms. The highest DDI risk is typically seen with class 2/1B/3B compounds manifested by inhibition of either CYP metabolism or active hepatic uptake. Class 2 showed a wider range in AUC change likely due to a variety of enzymes involved. DDI risk for class 3A/4 is small and associated with inhibition of renal transporters., Conclusions: ECCS provides a framework to project ADME profiles and further enables prediction of victim DDI liabilities in drug discovery and development.

The Biopharmaceutical Classification System (BCS) has been a prognostic tool for assessing the potential effects of formulation on the human drug oral bioavailability. When used in conjunction with in vitro dissolution tests, the BCS can support the prediction of in vivo product performance and the development of mechanistic models that support formulation assessments through the generation of "what if" scenarios. To date, the applicability of existing human BCS criteria has not been evaluated in dogs, thereby limiting its use in canine drug development. Therefore, we examined 50 drugs for which absolute bioavailability (F) was available both in dogs and humans. The drugs were also evaluated for any potential association between solubility (calculated from the dose number, Do) or lipophilicity (LogP) and F in dogs. In humans, solubility is determined in 250 mL of fluid. However, the appropriate volume for classifying drug solubility in dogs has not been established. In this analysis, the estimated volume of a water flush administered to fasted dogs (6 mL) and a volume of 250 mL scaled to a Beagle dog (35 mL) were examined. In addition, in humans, a Do value greater than 1.0 is used to define a compound as highly soluble and a LogP value greater than 1.72 as high permeability. These same criteria were applied for defining highly soluble and highly permeable in dogs. Whether using 35 or 6 mL to determine Do, the canine solubility classification remained unchanged for all but seven compounds. There were no clear associations between a drug's F in dogs and humans or between the canine value of F and either its human BCS classification, its LogP value, or the canine Do estimate. There was a tendency for those drugs with canine values of F equal to or greater than 80% to have LogP values equal to or greater than 1.0. Exceptions to this observation tended to be those compounds known to be absorbed via mechanisms other than passive diffusion (e.g., via transporters or paracellular transporters). Although there are limitations to the approach used in this study, the results of our assessment strongly suggest that the human BCS classification system requires substantial modification before it can be reliably applied to dogs.

Intestinal drug permeability has been recognized as a critical determinant of the fraction dose absorbed, with direct influence on bioavailability, bioequivalence and biowaiver. The purpose of this research was to compare intestinal permeability values obtained by two different intestinal rat perfusion methods: the single-pass intestinal perfusion (SPIP) model and the Doluisio (closed-loop) rat perfusion method. A list of 15 model drugs with different permeability characteristics (low, moderate, and high, as well as passively and actively absorbed) was constructed. We assessed the rat intestinal permeability of these 15 model drugs in both SPIP and the Doluisio methods, and evaluated the correlation between them. We then evaluated the ability of each of these methods to predict the fraction dose absorbed (Fabs) in humans, and to assign the correct BCS permeability class membership. Excellent correlation was obtained between the two experimental methods (r(2)=0.93). An excellent correlation was also shown between literature Fabs values and the predictions made by both rat perfusion techniques. Similar BCS permeability class membership was designated by literature data and by both SPIP and Doluisio methods for all compounds. In conclusion, the SPIP model and the Doluisio (closed-loop) rat perfusion method are both equally useful for obtaining intestinal permeability values that can be used for Fabs prediction and BCS classification., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Biliary excretion is an important route of elimination for many drugs, yet measuring the extent of biliary elimination is difficult, invasive, and variable. Biliary elimination has been quantified for few drugs with a limited number of subjects, who are often diseased patients. An accurate prediction of which drugs or new molecular entities are significantly eliminated in the bile may predict potential drug-drug interactions, pharmacokinetics, and toxicities. The Biopharmaceutics Drug Disposition Classification System (BDDCS) characterizes significant routes of drug elimination, identifies potential transporter effects, and is useful in understanding drug-drug interactions. Class 1 and 2 drugs are primarily eliminated in humans via metabolism and will not exhibit significant biliary excretion of parent compound. In contrast, class 3 and 4 drugs are primarily excreted unchanged in the urine or bile. Here, we characterize the significant elimination route of 105 orally administered class 3 and 4 drugs. We introduce and validate a novel model, predicting significant biliary elimination using a simple classification scheme. The model is accurate for 83% of 30 drugs collected after model development. The model corroborates the observation that biliarily eliminated drugs have high molecular weights, while demonstrating the necessity of considering route of administration and extent of metabolism when predicting biliary excretion. Interestingly, a predictor of potential metabolism significantly improves predictions of major elimination routes of poorly metabolized drugs. This model successfully predicts the major elimination route for poorly permeable/poorly metabolized drugs and may be applied prior to human dosing.

The ability to predict in vivo response of an oral dosage form based on an in vitro technique has been a sought after goal of the pharmaceutical scientist. Dissolution testing that demonstrates discrimination to various critical formulations or process attributes provides a sensitive quality check that may be representative or may be overpredictive of potential in vivo changes. Dissolution methodology with an established in vitro-in vivo relationship or correlation may provide the desired in vivo predictability. To establish this in vitro-in vivo link, a clinical study must be performed. In this article, recommendations are given in the selection of batches for the clinical study followed by potential outcome scenarios. The investigation of a Level C in vitro-in vivo correlation (IVIVC), which is the most common correlation for immediate-release oral dosage forms, is presented. Lastly, an IVIVC case study involving a biopharmaceutical classification system class IV compound is presented encompassing this strategy and techniques., (© 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.)

In this paper we analyse how the biopharmaceutics classification system (BCS) has been used to date. A survey of the literature resulted in a compilation of 242 compounds for which BCS classes were reported. Of these, 183 compounds had been reported to belong to one specific BCS class whereas 59 compounds had been assigned to multiple BCS classes in different papers. Interestingly, a majority of the BCS class 2 compounds had fraction absorbed (FA) values >85%, indicating that they were completely absorbed after oral administration. Solubility was computationally predicted at pH 6.8 for BCS class 2 compounds to explore the impact of the pH of the small intestine, where most of the absorption occurs, on the solubility. In addition, the solubilization capacity of lipid aggregates naturally present in the intestine was studied computationally and experimentally for a subset of 12 compounds. It was found that all acidic compounds with FA>85% were completely dissolved in the pH of the small intestine. Further, lipids at the concentration used in fasted state simulated intestinal fluid (FaSSIF) dissolved the complete dose given of the most lipophilic (logD6.5>3) compounds studied. Overall, biorelevant dissolution media (pure buffer of intestinal pH or FaSSIF) identified that for 20 of the 29 BCS class 2 compounds with FA>85% the complete dose given orally would be dissolved. These results indicate that a more relevant pH restriction for acids and/or dissolution medium with lipids present better forecast solubility-limited absorption in vivo than the presently used BCS solubility criterion. The analysis presented herein further strengthens the discussion on the requirement of more physiologically relevant dissolution media for the in vitro solubility classification performed to reach the full potential of the BCS., (Copyright © 2013 Elsevier B.V. All rights reserved.)

The Biopharmaceutics Classification System (BCS) has found widespread utility in drug discovery, product development and drug product regulatory sciences. The classification scheme captures the two most significant factors influencing oral drug absorption; solubility and intestinal permeability and it has proven to be a very useful and a widely accepted starting point for drug product development and drug product regulation. The mechanistic base of the BCS approach has, no doubt, contributed to its wide spread acceptance and utility. Nevertheless, underneath the simplicity of BCS are many detailed complexities, both in vitro and in vivo which must be evaluated and investigated for any given drug and drug product. In this manuscript we propose a simple extension of the BCS classes to include sub-specification of acid (a), base (b) and neutral (c) for classes II and IV. Sub-classification for Classes I and III (high solubility drugs as currently defined) is generally not needed except perhaps in border line solubility cases. It is well known that the , pKa physical property of a drug (API) has a significant impact on the aqueous solubility dissolution of drug from the drug product both in vitro and in vivo for BCS Class II and IV acids and bases, and is the basis, we propose for a sub-classification extension of the original BCS classification. This BCS sub-classification is particularly important for in vivo predictive dissolution methodology development due to the complex and variable in vivo environment in the gastrointestinal tract, with its changing pH, buffer capacity, luminal volume, surfactant luminal conditions, permeability profile along the gastrointestinal tract and variable transit and fasted and fed states. We believe this sub-classification is a step toward developing a more science-based mechanistic in vivo predictive dissolution (IPD) methodology. Such a dissolution methodology can be used by development scientists to assess the likelihood of a formulation and dosage form functioning as desired in humans, can be optimized along with parallel human pharmacokinetic studies to set a dissolution methodology for Quality by Design (QbD) and in vitro-in vivo correlations (IVIVC) and ultimately can be used as a basis for a dissolution standard that will ensure continued in vivo product performance., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Drug-induced liver injury (DILI) is a frequent cause for the termination of drug development programs and a leading reason of drug withdrawal from the marketplace. Unfortunately, the current preclinical testing strategies, including the regulatory-required animal toxicity studies or simple in vitro tests, are insufficiently powered to predict DILI in patients reliably. Notably, the limited predictive power of such testing strategies is mostly attributed to the complex nature of DILI, a poor understanding of its mechanism, a scarcity of human hepatotoxicity data and inadequate bioinformatics capabilities. With the advent of high-content screening assays, toxicogenomics and bioinformatics, multiple end points can be studied simultaneously to improve prediction of clinically relevant DILIs. This review focuses on the current state of efforts in developing predictive models from diverse data sources for potential use in detecting human hepatotoxicity, and also aims to provide perspectives on how to further improve DILI prediction.

The Biopharmaceutics Classification System (BCS) was developed to facilitate estimation of the in vivo pharmacokinetic performance of drugs from human intestinal permeability and solubility. However, the measurement of human in vivo intestinal permeability, unlike that of solubility, is problematic and inefficient. Thus, rat in vitro intestinal permeability results obtained via the Ussing chamber technique are often used instead. However, these data could be unreliable due to difficulty in maintaining the viability of the dissected intestinal membrane in the Ussing chamber. Therefore, a more efficient method to obtain a reliable in vitro permeability is mandatory. Here, we propose a new approach by introducing a novel factor called the permeability ratio (PR). Basically, PR is a rat in vitro intestinal permeability obtained from the Ussing chamber, which is then corrected by the permeability of lucifer yellow, a paracellular permeability marker. To prove the validity of the method, 12 model drugs representing different BCS classes were tested, and the correlation with human in vivo intestinal permeability was high. More importantly, the new method perfectly classified all 12 model drugs. The results indicate that PR is a reliable factor with high correlation to human in vivo intestinal permeability, which can further be used to accurately predict the BCS classification.

Human effective intestinal membrane permeability (Peff) is one of the two important indicators for drug classification according to the Biopharmaceutical Classification System (BCS), and contributes greatly to the performance of oral drug absorption. Here, a structure-based in silico predictive model of Peff was developed successfully to facilitate in silico BCS classification in the early stage of drug discovery, even before the compound was synthesized. The quantitative structure-Peff relationship for 30 drugs was constructed based on seven structural parameters. Then the model was built by the multiple linear regression method and internally validated by the residual analysis, the normal probability-probability plot and the Williams plot. For the entire data set, the R² and adjusted R² values were 0.782 and 0.712, respectively. The results indicated that the fitted model was robust, stable and satisfied all the prerequisites of the regression models. As for the 102 tested drugs, the predicted Peff values had a good correlation with the experimental human absorbed fraction (Fa). This model was also used to perform high/low Peff classification for 57 drugs that have been classified according to the BCS, and 72% of drugs could be classified correctly, indicating that the developed model can be used for rapid BCS classification in the early stages of drug discovery., (Copyright © 2013 John Wiley & Sons, Ltd.)

Purpose: To develop a dose dependent version of BCS and identify a critical dose after which the amount absorbed is independent from the dose., Methods: We utilized a mathematical model of drug absorption in order to produce simulations of the fraction of dose absorbed (F) and the amount absorbed as function of the dose for the various classes of BCS and the marginal cases in between classes., Results: Simulations based on the mathematical model of F versus dose produced patterns of a constant F throughout a wide range of doses for drugs of Classes I, II and III, justifying biowaiver claim. For Classes I and III the pattern of a constant F stops at a critical dose Dose(cr) after which the amount of drug absorbed, is independent from the dose. For doses higher than Dose(cr), Class I drugs become Class II and Class III drugs become Class IV. Dose(cr) was used to define an in vivo effective solubility as S(eff) = Dose(cr)/250 ml. Literature data were used to support our simulation results., Conclusions: A new biopharmaceutic classification of drugs is proposed, based on F, separating drugs into three regions, taking into account the dose, and Dose(cr), while the regions for claiming biowaiver are clearly defined.

Analytical pharmacology strives to compare pharmacological data to detailed quantitative models. The most famous tool in this regard is the Black/Leff operational model, which can be used to quantify agonism in a test system and predict it in any other system. Here we give examples of how and where analytical pharmacology has been used to classify drugs and predict mechanism of action in pharmacology. We argue for the importance of analytical pharmacology in drug classification and in prediction of drug mechanisms of action. Although some of the specifics of Black's models have been updated to account for new developments, the principles of analytical pharmacology should shape drug discovery for many years to come., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Current assessment of drug-drug interaction (DDI) prediction success is based on whether predictions fall within a two-fold range of the observed data. This strategy results in a potential bias toward successful prediction at lower interaction levels [ratio of the area under the concentration-time profile (AUC) in the presence of inhibitor/inducer compared with control is <2]. This scenario can bias any assessment of different DDI prediction algorithms if databases contain large proportion of interactions in this lower range. Therefore, the current study proposes an alternative method to assess prediction success with a variable prediction margin dependent on the particular AUC ratio. The method is applicable for assessment of both induction and inhibition-related algorithms. The inclusion of variability into this predictive measure is also considered using midazolam as a case study. Comparison of the traditional two-fold and the new predictive method was performed on a subset of midazolam DDIs collated from previous databases; in each case, DDIs were predicted using the dynamic model in Simcyp simulator. A 21% reduction in prediction accuracy was evident using the new predictive measure, in particular at the level of no/weak interaction (AUC ratio <2). However, inclusion of variability increased the prediction success at these levels by two-fold. The trend of lower prediction accuracy at higher potency of DDIs reported in previous studies is no longer apparent when predictions are assessed via the new predictive measure. Thus, the study proposes a more logical method for the assessment of prediction success and its application for induction and inhibition DDIs.

Bioavailability of orally administered drugs can be influenced by a number of factors including release from the formulation, dissolution, stability in the gastrointestinal (GI) environment, permeability through the gut wall and first-pass gut wall and hepatic metabolism. Although there are various enzymes in the gut wall which may contribute to gut first pass metabolism, Cytochrome P450 (CYP) 3A has been shown to play a major role. The efflux transporter P-glycoprotein (P-gp; MDR1/ABCB1) is the most extensively studied drug efflux transporter in the gut and might have a significant role in the regulation of GI absorption. Although not every CYP3A substrate will have a high extent of gut wall first-pass extraction, being a substrate for the enzyme increases the likelihood of a higher first-pass extraction. Similarly, being a P-gp substrate does not necessarily pose a problem with the gut wall absorption however it may reduce bioavailability in some cases (e.g. when drug has low passive permeability). An on-going debate has focused on the issue of the interplay between CYP3A and P-gp such that high affinity to P-gp increases the exposure of drug to CYP3A through repeated cycling via passive diffusion and active efflux, decreasing the fraction of drug that escapes first pass gut metabolism (F(G)). The presence of P-gp in the gut wall and the high affinity of some CYP3A substrates to this transporter are postulated to reduce the potential for saturating the enzymes, thus increasing gut wall first-pass metabolism for compounds which otherwise would have saturated CYP3A. Such inferences are based on assumptions in the modelling of oral drug absorption. These models should be as mechanistic as possible and tractable using available in vitro and in vivo information. We review, through simulation, this subject and examine the interplay between gut wall metabolism and efflux transporters by studying the fraction of dose absorbed into enterocytes (F(a)) and F(G) via systematic variation of drug characteristics, in accordance with the Biopharmaceutics Classification System (BCS) within one of the most physiological models of oral drug absorption currently available, respectively ADAM. Variables studied included the intrinsic clearance (CLint) and the Michaelis-Menten Constant (Km) for CYP3A4 and P-gp (C(Lint-CYP3A4) and K(m-CYP3A4), CL(int-P-gp) and K(m-P-gp)). The impact of CYP3A4 and P-gp intracellular topography were not investigated since a well-stirred enterocyte is assumed within ADAM. An increased CLint-CYP3A4 resulted in a reduced F(G) whereas an increase in C(Lint-P-gp) resulted in a reduced F(a), but interestingly decreased F(G) too. The reduction in FG was limited to certain conditions and was modest. Non-linear relationships between various parameters determining the permeability (e.g. P(app), C(Lint-P-gp,) and K(m-P-gp)) and gut wall metabolism (e.g. C(Lint-CYP3A4,) K(m-CYP3A4)) resulted in disproportionate changes in F(G) compared to the magnitude of singular effects. The results suggest that P-gp efflux decreases enterocytic drug concentration for drugs given at reasonably high dose which possess adequate passive permeability (high P(app)), by de-saturating CYP3A4 in the gut resulting in a lower F(G). However, these findings were observed only in a very limited area of the parameters space matching very few therapeutic drugs (a group with very high metabolism, high turn-over by efflux transporters and low F(a)). The systematic approach in this study enabled us to recognise the combination of parameters values where the potential interplay between metabolising enzymes and efflux transporters is expected to be highest, using a realistic range of parameter values taken from an intensive literature search.

While the oral exposure continues to be the major focus, the chemical space of recent drug discovery is apparently trending towards more hydrophilic libraries, due to toxicity and drug-interactions issues usually reported with lipophilic drugs. This trend may bring in challenges in optimizing the membrane permeability and thus the oral absorption of new chemical entities. It is now apparent that the influx transporters such as peptide transporter 1 (PepT1), organic-anion transporting polypeptides (OATPs), monocarboxylate transporters (MCT1) facilitate, while efflux pumps (e.g. P-glycoprotein (P-gp), breast cancer resistance protein (BCRP)) limit oral absorption of drugs. This review will focus on intestinal transporters that may be targeted to achieve optimal clinical oral plasma exposure for hydrophilic and polar drugs. The structure, mechanism, structure-activity relationships and the clinical examples on the functional role of these transporters in the drug absorption was discussed. Physicochemical properties, lipophilicity and hydrogen-bonding ability, show good correlation with transport activity for efflux pumps. Although several attempts were made to describe the structural requirements based on pharmacophore modeling, lack of crystal structure of transporters impeded identification of definite properties for transporter affinity and favorable transport activity. Furthermore, very few substrate drug datasets are currently available for the influx transporters to derive any clear relationships. Unfortunately, gaps also exist in the translation of in vitro end points to the clinical relevance of the transporter(s) involved. However, it may be qualitatively generalized that targeting intestinal transporters are relevant for drugs with high solubility and/or low passive permeability i.e. a class of compounds identified as Class III and Class IV according to the Biopharmaceutic Classification System (BCS) and the Biopharmaceutic Drug Disposition Classification System (BDDCS). A careful considerations to oral dose based on the transporter clearance (V(max)/K(m)) capacity is needed in targeting a particular transporter. For example, low affinity and high capacity uptake transporters such as PEPT1 and MCT1 may be targeted for high oral dose drugs.

This study examines whether algorithms to predict brain penetration of 88 drug candidates could benefit from inclusion of PAMPA data such as Peff, flux and membrane retention. Specifically the ability to fit experimentally derived LogBB data with PAMPA information and compound related physicochemical and structural parameters was assessed. Collected data were analyzed by partial least square analysis and various regression models for LogBB. Four PAMPA methodologies were evaluated in this study including: (1) a PAMPA-BLM (black lipid membrane) model, (2) a PAMPA-DS (double sink) model, (3) a PAMPA-BBB (blood-brain barrier) model and (4) a PAMPA-BBB-UWL (unstirred water layer). Additionally, plasma protein binding (PPB) experiments and a Caco-2 assay were performed to determine the unbound fraction in plasma and the efflux ratio, respectively, for subsets of the selected compounds. This information was combined with the obtained PAMPA data in an effort to improve the predictions of LogBB. Taken in aggregate, the results presented, suggest that the PAMPA-BLM parameters are the most important contributors to predict the LogBB. The optimized multiple linear regression (MLR) relationship including the PAMPA-BLM properties demonstrated a slightly improved prediction compared to the model without the PAMPA-BLM parameters. Including the plasma protein binding of 15 compounds resulted in a significantly improved PAMPA-BLM prediction of LogBB, while integrating the efflux ratio with PAMPA-BLM or PAMPA-BBB Peff values, resulted in improved classification of brain permeable [BBB + (LogBB >or= 0)] and impermeable [BBB--(LogBB < 0)] compounds.

The diffusion layer model of drug dissolution is used for the simulation of oral drug absorption as well as for the analysis of experimental data. The governing role of saturation solubility in the rate of dissolution makes this parameter predominant for biopharmaceutical classification purposes. The hypothesis models and criteria associated with the use of solubility and dissolution for the biopharmaceutical classification of compounds and marketed drugs are reviewed in this article. The complex hydrodynamics in the in vitro dissolution apparatuses as well as the motility in the gastrointestinal tract do not allow the application of the diffusion layer model in these systems, as this has been built and verified in the rotating disk device. The solubilizing capacity of gastrointestinal fluids media is higher than the aqueous saturation solubility usually reported and used for biopharmaceutical purposes. Emphasis is given on the reaction-limited model of dissolution which provides a useful alternative not based on diffusion principles. Model independent dissolution parameters are more useful for regulators as our knowledge for the dissolution mechanism(s) under in vivo conditions is limited.

A Biopharmaceutics Drug Disposition Classification System (BDDCS) was proposed to serve as a basis for predicting the importance of transporters in determining drug bioavailability and disposition. BDDCS may be useful in predicting: routes of drug elimination; efflux and absorptive transporters effects on oral absorption; when transporter-enzyme interplay will yield clinically significant effects (e.g. low drug bioavailability and drug-drug interactions); and transporter effects on post-absorptive systemic drug levels following oral and i.v. dosing. For highly soluble, highly permeable Class 1 compounds, metabolism is the major route of elimination and transporter effects on drug bioavailability and hepatic disposition are negligible. In contrast for the poorly permeable Class 3 and 4 compounds, metabolism only plays a minor role in drug elimination. Uptake transporters are major determinants of drug bioavailability for these poorly permeable drugs and both uptake and efflux transporters could be important for drug elimination. Highly permeable, poorly soluble, extensively metabolized Class 2 compounds present the most complicated relationship in defining the impact of transporters due to a marked transporter-enzyme interplay. Uptake transporters are unimportant for Class 2 drug bioavailability, (ensure space after,) but can play a major role in hepatic and renal elimination. Efflux transporters have major effects on drug bioavailability, absorption, metabolism and elimination of Class 2 drugs. It is difficult to accurately characterize drugs in terms of the high permeability criteria, i.e. > or =90% absorbed. We suggest that extensive metabolism may substitute for the high permeability characteristic, and that BDDCS using elimination criteria may provide predictability in characterizing drug disposition profiles for all classes of compounds.

Aiming at the prediction of pleiotropic effects of drugs, we have investigated the multilabel classification of drugs that have one or more of 100 different kinds of activity labels. Structural feature representation of each drug molecule was based on the topological fragment spectra method, which was proposed in our previous work. Support vector machine (SVM) was used for the classification and the prediction of their activity classes. Multilabel classification was carried out by a set of the SVM classifiers. The collective SVM classifiers were trained with a training set of 59,180 compounds and validated by another set (validation set) of 29,590 compounds. For a test set that consists of 9,864 compounds, the classifiers correctly classified 80.8% of the drugs into their own active classes. The SVM classifiers also successfully performed predictions of the activity spectra for multilabel compounds.

Purpose: The objective was to establish in vitro passive permeability (Pe) vs in vivo fraction absorbed (fa)-relationships for each passage through the human intestine, liver, renal tubuli and brain, and develop a Pe-based ADME/PK classification system (PCS)., Materials and Methods: Pe- and intestinal fa-data were taken from an available data set. Hepatic fa was calculated based on extraction ratios of the unbound fraction of drugs (with support from animal in vivo uptake data). Renal fa (reabsorption) was estimated using renal pharmacokinetic data, and brain fa was predicted using animal in vitro and in vivo brain Pe-data. Hepatic and intestinal fa-data were used to predict bile excretion potential., Results: Relationships were established, including predicted curves for bile excretion potential and minimum oral bioavailability, and a 4-Class PCS was developed: I (very high Pe; elimination mainly by metabolism); II (high Pe) and III (intermediate Pe and incomplete fa); IV (low Pe and fa). The system enables assessment of potential drug-drug transport interactions, and drug and metabolite organ trapping., Conclusions: The PCS and high quality Pe-data (with and without active transport) are believed to be useful for predictions and understanding of ADME/PK, elimination routes, and potential interactions and organ trapping/toxicity in humans.

A drug intended for use in humans should have an ideal balance of pharmacokinetics and safety, as well as potency and selectivity. Unfavorable pharmacokinetics can negatively affect the clinical development of many otherwise promising drug candidates. A variety of in silico ADME (absorption, distribution, metabolism, and excretion) models are receiving increased attention due to a better appreciation that pharmacokinetic properties should be considered in early phases of the drug discovery process. Human oral bioavailability is an important pharmacokinetic property, which is directly related to the amount of drug available in the systemic circulation to exert pharmacological and therapeutic effects. In the present work, hologram quantitative structure-activity relationships (HQSAR) were performed on a training set of 250 structurally diverse molecules with known human oral bioavailability. The most significant HQSAR model (q(2)=0.70, r(2)=0.93) was obtained using atoms, bond, connection, and chirality as fragment distinction. The predictive ability of the model was evaluated by an external test set containing 52 molecules not included in the training set, and the predicted values were in good agreement with the experimental values. The HQSAR model should be useful for the design of new drug candidates having increased bioavailability as well as in the process of chemical library design, virtual screening, and high-throughput screening.

Research on Selective Estrogen Receptor Modulators (SERMs) has been driven by interest in discovering target selective molecules. In view of such significance, the present work explored the pharmacophores of estrogen receptor (ER) subtypes specific binding affinities of diverse compounds belonging to the category of bridged bicyclic-1,1-diarylethylene derivatives. Implementing classical QSAR and CATALYST based space-modeling approaches, it has been explored that attachment of aryl ring systems to unsaturated linkages, availability of phenolic hydroxyl group, global hydrophobicity, and stereochemistry of certain functional groups might be important for governing the subtype specific estrogenic behavior of this group of compounds. Supplementing this deduction, critical interfeature distances between hydrogen bond acceptor, hydrophobic, and ring aromatic features along with steric influence are found to primarily influence the ER-subtypes specific binding of this series of compounds.

Hippocampus is the brain structure, vital for episodic and declarative memory. Atrophy of the human hippocampus is seen in a variety of psychiatric and neurological disorders e.g. recurrent depression, schizophrenia, bipolar disorder, post-traumatic stress disorder, epilepsy, head injury, and Alzheimer's disease (AD). Importantly, aging hippocampus also undergoes atrophy. In many instances, for example, AD, the atrophy precedes the development of symptoms while in others, there is a temporal relationship between atrophy and symptomatology. The presence of atrophied hippocampus is one of the most consistent features of many common psychiatric disorders. Several factors contribute to this atrophy. Stress is one of the most profound factors implicated and the mechanisms involve glucocorticoids, serotonin, excitatory amino acids etc. Hippocampal formation as a whole can undergo atrophy or its individual structural components e.g. apical dendrities can exhibit atrophy. Several drugs of unrelated classes have been shown to prevent atrophy indicating heterogenous manner in which hippocampal atrophy is produced. These include, tianeptine (affects structural plasticity in hippocampus and is an effective antidepressant); phenytoin (antiseizure and neuroprotective); fluoxetine (downregulates neurodegenerative enzyme and increases neuroprotective hippocampal S100 beta); lithium (neuroprotective and antiapoptotic); tricyclic antidepressants (increase hippocampal neurogenesis); antipsychotics (reduce hippocampal neuronal suppression); sodium valproate (increases neurogenesis) and mifepristone (antioxidant, neuroprotective and anti-glucocorticoid). Now the most important question is: to what extent does the hippocampal atrophy play a role in the genesis of symptoms of diseases or their progression? And if it does, can we achieve the same degree of prevention or reversal seen in experimental animals, in humans also. An even more important question is: whether the prevention of atrophy would be clinically useful in affecting disease, viz slowing its progression, reducing morbidity, complications or positively affecting the outcome of one or more of its clinically important aspects. If the answer to this is yes, we would have to know at what stage of the disease we use the drugs, dose, duration, follow-up and efficacy. The use of these drugs in the above mentioned conditions can not only test the potential of atrophy as a future drug target, but could also help in learning more about the hippocampus in both health and diseases.

(Q)SAR models can be used to reduce animal testing as well as to minimise the testing costs. In particular, classification models have been widely used for estimating endpoints with binary activity. The aim of the present study was to develop and validate a classification-based quantitative structure-activity relationship (QSAR) model for endocrine disruption, based on interpretable mechanistic descriptors related to estrogenic gene activation. The model predicts the presence or absence of estrogenic activity according to a pre-defined cut-off in activity as determined in a recombinant yeast assay. The experimental data was obtained from the literature. A two-descriptor classification model was developed that has the form of a decision tree. The predictivity of the model was evaluated by using an external test set and by taking into account the limitations associated with the applicability domain (AD) of the model. The AD was determined as coverage of the model descriptor space. After removing the compounds present in the training set and the compounds outside of the AD, the overall accuracy of classification of the test chemicals was used to assess the predictivity of the model. In addition, the model was shown to meet the OECD Principles for (Q)SAR Validation, making it potentially useful for regulatory purposes.

A topological substructural molecular design approach (TOPS-MODE) has been used to predict whether a given compound is a P-glycoprotein (P-gp) substrate or not. A linear discriminant model was developed to classify a data set of 163 compounds as substrates or nonsubstrates (91 substrates and 72 nonsubstrates). The final model fit the data with sensitivity of 82.42% and specificity of 79.17%, for a final accuracy of 80.98%. The model was validated through the use of an external validation set (40 compounds, 22 substrates and 18 nonsubstrates) with a 77.50% of prediction accuracy; fivefold full cross-validation (removing 40 compounds in each cycle, 80.50% of good prediction) and the prediction of an external test set of marketed drugs (35 compounds, 71.43% of good prediction). This methodology evidenced that the standard bond distance, the polarizability and the Gasteiger-Marsilli atomic charge affect the interaction with the P-gp; suggesting the capacity of the TOPS-MODE descriptors to estimate the P-gp substrates for new drug candidates. The potentiality of the TOPS-MODE approach was assessed with a family of compounds not covered by the original training set (6-fluoroquinolones), and the final prediction had a 77.7% of accuracy. Finally, the positive and negative substructural contributions to the classification of 6-fluoroquinolones, as P-gp substrates, were identified; evidencing the possibilities of the present approach in the lead generation and optimization processes., (Copyright 2006 Wiley-Liss, Inc. and the American Pharmacists Association.)

The current mini-review explains how fragmental methods (FMs) can be used in the analysis and prediction of physicochemical properties and biological activities. The considered properties include log P, solubility, pK(a), intestinal permeability, P-gp substrate specificity and toxicity. The focus will be a description of a "mechanistic" approach, which implies a gradual reduction of alternative explanations for any property or activity. This means a flexible construction of fragmental parameters using large amounts of experimental data. Since biological activities involve multiple (unknown) target macromolecules with multiple binding modes, a stepwise classification (C-SAR) analysis is most useful. It involves the following procedures: (i). construction of physicochemical profiles using parameters that can be reliably predicted, (ii). identification of reactive functional groups and the largest active skeletons, (iii). generalization of these groups and skeletons in terms of "site-specific physicochemical profiling". This entails a dynamic construction of 2D pharmacophores that can be converted into 3D models.

Default risk assessment procedures use threshold models for non-carcinogens and a non-threshold model for carcinogens. This a priori distinction reflects the fact that the default procedures do not consider mechanisms of action of specific chemicals. When mechanisms are considered, the distinction is not necessary. Starting with the premise that the goal of risk assessment is to identify actual risk for specific chemicals, three major, generic components of the overall mechanism translating exposure into a response of regulatory interest are identified. These are the specific mechanisms linking (1) exposure with dose to target tissue, (2) target tissue dose with short-term responses such as cytolethality or mutation, and (3) short-term responses with ensuing long-term responses such as cancer or cirrhosis. (Short-term responses may be regulatory end points of interest, or they may be intermediate steps on the way to longer-term sequelae). On-going research on formaldehyde and chloroform is described to illustrate how these three components of the overall mechanism can be examined experimentally and used in specific models. The impact of mechanism-based risk assessment on uncertainty is also considered. Uncertainty is a function of the extent to which the model used for risk assessment misspecifies the actual mechanism of action for the chemical in question. There is a trade-off between (a) mechanism-based models that may reduce uncertainty but are expensive and time-consuming to develop and (b) default models that are not chemical-specific but can be used with minimal data sets. Experience with mechanism-based risk assessment may allow modification of default procedures to minimize this trade-off. A future default procedure for carcinogen risk assessment might allow specification of mode of action. For example, while DNA reactive-carcinogens would still be assumed to have linear low-dose risk, carcinogens acting through purely cytotoxic mechanisms might be assumed to have sharply non-linear or even threshold dose-response curves.