Computational models to assign biopharmaceutics drug disposition classification from molecular structure

Investigating pharmacokinetic profiles of Centella asiatica using machine learning and PBPK modelling

Physiologically based pharmacokinetic (PBPK) modeling serves as a valuable tool for determining the distribution and disposition of substances in the body of an organism. It involves a mathematical representation of the interrelationships among crucial physiological, biochemical, and physicochemical parameters. A lack of the values of pharmacokinetic parameters can be challenging in constructing a PBPK model. Herein, we propose an artificial intelligence framework to evaluate a key pharmacokinetic parameter, the intestinal effective permeability (Peff). The publicly available Peff dataset was utilized to develop regression machine learning models. The XGBoost model demonstrates the best test accuracy of R-squared (R2, coefficient of determination) of 0.68. The model is then applied to compute the Peff of asiaticoside and madecassoside, the parent compounds found in Centella asiatica. Subsequently, PBPK modeling was conducted to evaluate the biodistribution of the herbal substances following oral administration in a rat model. The simulation results were evaluated and validated, which agreed with the existing in vivo studies in rats. This in silico pipeline presents a potential approach for investigating the pharmacokinetic parameters and profiles of drugs or herbal substances, which can be used independently or integrated into other modeling systems.

Selected Aspects of the Analytical and Pharmaceutical Profiles of Nifurtimox

Nifurtimox is a nitroheterocyclic drug employed for treatment of trypanosomiases (Chagas disease and West African sleeping sickness); its use for certain cancers has also been assessed. Despite having been in the market for over 50 years, knowledge of nifurtimox is still fragmentary and incomplete. Relevant aspects of the chemistry and biology of nifurtimox are reviewed to summarize the current knowledge of this drug. These comprise its chemical synthesis and the preparation of some analogues, as well as its chemical degradation. Selected physical data and physicochemical properties are also listed, along with different approaches toward the analytical characterization of the drug, including electrochemical (polarography, cyclic voltammetry), spectroscopic (ultraviolet-visible, nuclear magnetic resonance, electron spin resonance), and single crystal X-ray diffractometry. The array of polarographic, ultraviolet-visible spectroscopic, and chromatographic methods available for the analytical determination of nifurtimox (in bulk drug, pharmaceutical formulations, and biological samples), are also presented and discussed, along with chiral chromatographic and electrophoretic alternatives for the separation of the enantiomers of the drug. Aspects of the drug likeliness of nifurtimox, its classification in the Biopharmaceutical Classification System, and available pharmaceutical formulations are detailed, whereas pharmacological, chemical, and biological aspects of its metabolism and disposition are discussed.

Emerging Role of Biopharmaceutical Classification and Biopharmaceutical Drug Disposition System in Dosage form Development: A Systematic Review

Biopharmaceutical classification system (BCS) is an advanced tool used for classifying medicines based on dissolution, water solubility, and intestinal permeability, which affect the absorption of active pharmaceutical ingredients (API) from immediate-release solid oral forms. It is useful to the formulation researchers to develop novel dosage forms based on modernistic rather than experimental approaches. The current review focuses on the fundamentals, objectives, guidance of BCS, characteristics of BCS drugs, their importance and applications of BCS. This review explains the challenges in drug development in terms of solubility and in vivo disposition. In the current review, new strategies for improving BCS II drug solubility as well as biopharmaceutical drug disposition properties which are utilized throughout the early stages of drug development and commercialization are mainly discussed.

Reliable Prediction of Caco-2 Permeability by Supervised Recursive Machine Learning Approaches

The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0.43–0.51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods. The model was validated for the preliminary estimation of the BCS/BDDCS class. The KNIME workflow developed to automate new drug prediction is freely available. The results suggest that this automated prediction platform is a reliable tool for identifying the most promising compounds with high intestinal permeability during the early stages of drug discovery.

Population pharmacokinetic model selection assisted by machine learning

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained using mean square error or binary cross-entropy loss. Machine learning provided a selection based only on statistical rules and achieved accurate selection. The minimization process of genetic algorithm was successful at allowing the algorithm to select plausible models. Neural network classification tasks achieved the most accurate results. Neural network regression tasks were less precise than neural network classification and genetic algorithm methods. The computational gain obtained by using machine learning was substantial, especially in the case of neural networks. We demonstrated that machine learning methods can greatly increase the efficiency of pharmacokinetic population model selection in case of large datasets or complex models requiring long run-times. Our results suggest that machine learning approaches can achieve a first fast selection of models which can be followed by more conventional pharmacometric approaches.

Why do few drug delivery systems to combat neglected tropical diseases reach the market? An analysis from the technology's stages

INTRODUCTION

Many drugs used to combat schistosomiasis, Chagas disease, and leishmaniasis (SCL) have clinical limitations such as: high toxicity to the liver, kidneys and spleen; reproductive, gastrointestinal, and heart disorders; teratogenicity. In this sense, drug delivery systems (DDSs) have been described in the literature as a viable option for overcoming the limitations of these drugs. An analysis of the level of development (TRL) of patents can help in determine the steps that must be taken for promising technologies to reach the market.

AREAS COVERED

This study aimed to analyze the stage of development of DDSs for the treatment of SCL described in patents. In addition, we try to understand the main reasons why many DDSs do not reach the market. In this study, we examined DDSs for drugs indicated by WHO and treatment of SCL, by performing a search for patents.

EXPERT OPINION

In this present work we provide arguments that support the hypothesis that there is a lack of integration between academia and industry to finance and continue research, especially the development of clinical studies. We cite the translational research consortia as the potential alternative for developing DDSs to combat NTDs.

Fast screening of covariates in population models empowered by machine learning

One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from supervised learning algorithms using importance scores. We compare various classical methods with three machine learning (ML) methods applied to NONMEM empirical Bayes estimates: random forest, neural networks (NNs), and support vector regression (SVR). The performance of the ML models is assessed using receiver operating characteristic (ROC) curves. The F1 score, which measures test accuracy, is used to compare ML and PMX approaches. Methods are applied to different scenarios of covariate influence based on simulated pharmacokinetics data. ML achieved similar or better F1 scores than stepwise covariate modeling (SCM) and conditional sampling for stepwise approach based on correlation tests (COSSAC). Correlations between covariates and the number of false covariates does not affect the performance of any method, but effect size has an impact. Methods are not equivalent with respect to computational speed; SCM is 30 and 100-times slower than NN and SVR, respectively. The results are validated in an additional scenario involving 100 covariates. Taken together, the results indicate that ML methods can greatly increase the efficiency of population covariate model building in the case of large datasets or complex models that require long run-times. This can provide fast initial covariate screening, which can be followed by more conventional PMX approaches to assess the clinical relevance of selected covariates and build the final model.

The Biopharmaceutics Classification System (BCS) and the Biopharmaceutics Drug Disposition Classification System (BDDCS): Beyond guidelines

The recent impact of the Biopharmaceutics Classification System (BCS) and the Biopharmaceutics Drug Disposition Classification System (BDDCS) on relevant scientific advancements is discussed. The major advances associated with the BCS concern the extensive work on dissolution of poorly absorbed BCS class II drugs in nutritional liquids (e.g. milk, peanut oil) and biorelevant media for the accurate prediction of the rate and the extent of oral absorption. The use of physiologically based pharmacokinetic (PBPK) modeling as predictive tool for bioavailability is also presented. Since recent dissolution studies demonstrate that the two mechanisms (diffusion- and reaction-limited dissolution) take place simultaneously, the neglected reaction-limited dissolution models are discussed, regarding the biopharmaceutical classification of drugs. Solubility- and dissolution-enhancing formulation strategies based on the supersaturation principle to enhance the extent of drug absorption, along with the applications of the BDDCS to the understanding of disposition phenomena are reviewed. Finally, recent classification systems relevant either to the BCS or the BDDCS are presented. These include: i) a model independent approach based on %metabolism and the fulfilment (or not) of the current regulatory dissolution criteria, ii) the so called ΑΒΓ system, a continuous version of the BCS, and iii) the so-called Extended Clearance Classification System (ECCS). ECCS uses clearance concepts (physicochemical properties and membrane permeability) to classify compounds and differentiates from BDDCS by bypassing the measure of solubility (based on the assumption that since it inter-correlates with lipophilicity, it is not directly relevant to clearance mechanisms or elimination).

Application of data mining approach to identify drug subclasses based on solubility and permeability

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.

Design and Synthesis of Novel Deuterated Ligands Functionally Selective for the γ-Aminobutyric Acid Type A Receptor (GABAAR) α6 Subtype with Improved Metabolic Stability and Enhanced Bioavailability

Recent reports indicate that α6β2/3γ2 GABA_AR selective ligands may be important for the treatment of trigeminal activation-related pain and neuropsychiatric disorders with sensori-motor gating deficits. Based on 3 functionally α6β2/3γ2 GABA_AR selective pyrazoloquinolinones, 42 novel analogs were synthesized, and their in vitro metabolic stability and cytotoxicity as well as their in vivo pharmacokinetics, basic behavioral pharmacology, and effects on locomotion were investigated. Incorporation of deuterium into the methoxy substituents of the ligands increased their duration of action via improved metabolic stability and bioavailability, while their selectivity for the GABA_AR α6 subtype was retained. 8b was identified as the lead compound with a substantially improved pharmacokinetic profile. The ligands allosterically modulated diazepam insensitive α6β2/3γ2 GABA_ARs and were functionally silent at diazepam sensitive α1β2/3γ2 GABA_ARs, thus no sedation was detected. In addition, these analogs were not cytotoxic, which render them interesting candidates for treatment of CNS disorders mediated by GABA_AR α6β2/3γ2 subtypes.

Efficacy of Tilorone Dihydrochloride against Ebola Virus Infection

Tilorone dihydrochloride (tilorone) is a small-molecule, orally bioavailable drug that is used clinically as an antiviral outside the United States. A machine-learning model trained on anti-Ebola virus (EBOV) screening data previously identified tilorone as a potent in vitro EBOV inhibitor, making it a candidate for the treatment of Ebola virus disease (EVD). In the present study, a series of in vitro ADMET (absorption, distribution, metabolism, excretion, toxicity) assays demonstrated the drug has excellent solubility, high Caco-2 permeability, was not a P-glycoprotein substrate, and had no inhibitory activity against five human CYP450 enzymes (3A4, 2D6, 2C19, 2C9, and 1A2). Tilorone was shown to have 52% human plasma protein binding with excellent plasma stability and a mouse liver microsome half-life of 48 min. Dose range-finding studies in mice demonstrated a maximum tolerated single dose of 100 mg/kg of body weight. A pharmacokinetics study in mice at 2- and 10-mg/kg dose levels showed that the drug is rapidly absorbed, has dose-dependent increases in maximum concentration of unbound drug in plasma and areas under the concentration-time curve, and has a half-life of approximately 18 h in both males and females, although the exposure was ∼2.5-fold higher in male mice. Tilorone doses of 25 and 50 mg/kg proved efficacious in protecting 90% of mice from a lethal challenge with mouse-adapted with once-daily intraperitoneal (i.p.) dosing for 8 days. A subsequent study showed that 30 mg/kg/day of tilorone given i.p. starting 2 or 24 h postchallenge and continuing through day 7 postinfection was fully protective, indicating promising activity for the treatment of EVD.

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user’s own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery.

Toward predicting drug-induced liver injury: parallel computational approaches to identify multidrug resistance protein 4 and bile salt export pump inhibitors

Drug-induced liver injury (DILI) is an important cause of drug toxicity. Inhibition of multidrug resistance protein 4 (MRP4), in addition to bile salt export pump (BSEP), might be a risk factor for the development of cholestatic DILI. Recently, we demonstrated that inhibition of MRP4, in addition to BSEP, may be a risk factor for the development of cholestatic DILI. Here, we aimed to develop computational models to delineate molecular features underlying MRP4 and BSEP inhibition. Models were developed using 257 BSEP and 86 MRP4 inhibitors and noninhibitors in the training set. Models were externally validated and used to predict the affinity of compounds toward BSEP and MRP4 in the DrugBank database. Compounds with a score above the median fingerprint threshold were considered to have significant inhibitory effects on MRP4 and BSEP. Common feature pharmacophore models were developed for MRP4 and BSEP with LigandScout software using a training set of nine well characterized MRP4 inhibitors and nine potent BSEP inhibitors. Bayesian models for BSEP and MRP4 inhibition/noninhibition were developed with cross-validated receiver operator curve values greater than 0.8 for the test sets, indicating robust models with acceptable false positive and false negative prediction rates. Both MRP4 and BSEP inhibitor pharmacophore models were characterized by hydrophobic and hydrogen-bond acceptor features, albeit in distinct spatial arrangements. Similar molecular features between MRP4 and BSEP inhibitors may partially explain why various drugs have affinity for both transporters. The Bayesian (BSEP, MRP4) and pharmacophore (MRP4, BSEP) models demonstrated significant classification accuracy and predictability.

Comparing multilabel classification methods for provisional biopharmaceutics class prediction

The biopharmaceutical classification system (BCS) is now well established and utilized for the development and biowaivers of immediate oral dosage forms. The prediction of BCS class can be carried out using multilabel classification. Unlike single label classification, multilabel classification methods predict more than one class label at the same time. This paper compares two multilabel methods, binary relevance and classifier chain, for provisional BCS class prediction. Large data sets of permeability and solubility of drug and drug-like compounds were obtained from the literature and were used to build models using decision trees. The separate permeability and solubility models were validated, and a BCS validation set of 127 compounds where both permeability and solubility were known was used to compare the two aforementioned multilabel classification methods for provisional BCS class prediction. Overall, the results indicate that the classifier chain method, which takes into account label interactions, performed better compared to the binary relevance method. This work offers a comparison of multilabel methods and shows the potential of the classifier chain multilabel method for improved biological property predictions for use in drug discovery and development.

Progress in computational toxicology

INTRODUCTION

Computational methods have been widely applied to toxicology across pharmaceutical, consumer product and environmental fields over the past decade. Progress in computational toxicology is now reviewed.

METHODS

A literature review was performed on computational models for hepatotoxicity (e.g. for drug-induced liver injury (DILI)), cardiotoxicity, renal toxicity and genotoxicity. In addition various publications have been highlighted that use machine learning methods. Several computational toxicology model datasets from past publications were used to compare Bayesian and Support Vector Machine (SVM) learning methods.

RESULTS

The increasing amounts of data for defined toxicology endpoints have enabled machine learning models that have been increasingly used for predictions. It is shown that across many different models Bayesian and SVM perform similarly based on cross validation data.

DISCUSSION

Considerable progress has been made in computational toxicology in a decade in both model development and availability of larger scale or 'big data' models. The future efforts in toxicology data generation will likely provide us with hundreds of thousands of compounds that are readily accessible for machine learning models. These models will cover relevant chemistry space for pharmaceutical, consumer product and environmental applications.

Generative topographic mapping-based classification models and their applicability domain: application to the biopharmaceutics Drug Disposition Classification System (BDDCS)

Earlier (Kireeva et al. Mol. Inf. 2012, 31, 301-312), we demonstrated that generative topographic mapping (GTM) can be efficiently used both for data visualization and building of classification models in the initial D-dimensional space of molecular descriptors. Here, we describe the modeling in two-dimensional latent space for the four classes of the BioPharmaceutics Drug Disposition Classification System (BDDCS) involving VolSurf descriptors. Three new definitions of the applicability domain (AD) of models have been suggested: one class-independent AD which considers the GTM likelihood and two class-dependent ADs considering respectively, either the predominant class in a given node of the map or informational entropy. The class entropy AD was found to be the most efficient for the BDDCS modeling. The predominant class AD can be directly visualized on GTM maps, which helps the interpretation of the model.

Provisional classification and in silico study of biopharmaceutical system based on caco-2 cell permeability and dose number

Today, early characterization of drug properties by the Biopharmaceutics Classification System (BCS) has attracted significant attention in pharmaceutical discovery and development. In this direction, the present report provides a systematic study of the development of a BCS-based provisional classification (PBC) for a set of 322 oral drugs. This classification, based on the revised aqueous solubility and the apparent permeability across Caco-2 cell monolayers, displays a high correlation (overall 76%) with the provisional BCS classification published by World Health Organization (WHO). Current database contains 91 (28.3%) PBC class I drugs, 76 (23.6%) class II drugs, 97 (31.1%) class III drugs, and 58 (18.0%) class IV drugs. Other approaches for provisional classification of drugs have been surveyed. The use of a calculated polar surface area with a labetalol value as a high permeable cutoff limit and aqueous solubility higher than 0.1 mg/mL could be used as alternative criteria for provisionally classifying BCS permeability and solubility in early drug discovery. To develop QSPR models that allow screening PBC and BCS classes of new molecular entities (NMEs), 18 statistical linear and nonlinear models have been constructed based on 803 0-2D Dragon and 126 Volsurf+ molecular descriptors to classify the PBC solubility and permeability. The voting consensus model of solubility (VoteS) showed a high accuracy of 88.7% in training and 92.3% in the test set. Likewise, for the permeability model (VoteP), accuracy was 85.3% in training and 96.9% in the test set. A combination of VoteS and VoteP appropriately predicts the PBC class of drugs (overall 73% with class I precision of 77.2%). This consensus system predicts an external set of 57 WHO BCS classified drugs with 87.5% of accuracy. Interestingly, computational assignments of the PBC class reasonably correspond to the Biopharmaceutics Drug Disposition Classification System (BDDCS) allocations of drugs (accuracy of 63.3-69.8%). A screening assay has been simulated using a large data set of compounds in different drug development phases (1, 2, 3, and launched) and NMEs. Distributions of PBC forecasts illustrate the current status in drug discovery and development. It is anticipated that a combination of the QSPR approach and well-validated in vitro experimentations could offer the best estimation of BCS for NMEs in the early stages of drug discovery.

Keeping a critical eye on the science and the regulation of oral drug absorption: a review

This review starts with an introduction on the theoretical aspects of biopharmaceutics and developments in this field from mid-1950s to late 1970s. It critically addresses issues related to fundamental processes in oral drug absorption such as the complex interplay between drugs and the gastrointestinal system. Special emphasis is placed on drug dissolution and permeability phenomena as well as on the mathematical modeling of oral drug absorption. The review ends with regulatory aspects of oral drug absorption focusing on bioequivalence studies and the US Food and Drug Administration and European Medicines Agency guidelines dealing with Biopharmaceutics Classification System and Biopharmaceutic Drug Disposition Classification System.

Identification of novel breast cancer resistance protein (BCRP) inhibitors by virtual screening

Breast cancer resistance protein (BCRP; ABCG2) is an efflux transporter that plays an important role in multidrug resistance to antineoplastic drugs. The identification of drugs as BCRP inhibitors could aid in designing better therapeutic strategies for cancer treatment and will be critical for identifying potential drug-drug interactions. In the present study, we applied ligand-based virtual screening combined with experimental testing for the identification of novel drugs that can possibly interact with BCRP. Bayesian and pharmacophore models generated with known BCRP inhibitors were validated with an external test set. The resulting models were applied to predict new potential drug candidates from a database with more than 2000 FDA-approved drugs. Thirty-three drugs were tested in vitro for their inhibitory effects on BCRP-mediated transport of [(3)H]-mitoxantrone in MCF-7/AdrVp cells. Nineteen drugs were identified with significant inhibitory effect on BCRP transport function. The combined strategy of computational and experimental approaches in this paper has suggested potential drug candidates and thus represents an effective tool for rational identification of modulators of other proteins.

Unexpected performance of physical mixtures over solid dispersions on the dissolution behavior of benznidazole from tablets

This work investigated the feasibility of developing benznidazole (BZL) tablets, allowing fast, reproducible, and complete drug dissolution, by compressing BZL-Polyethylene Glycol (PEG) 6000 physical mixtures (PMs) and solid dispersions (SDs). SDs were prepared by the solvent evaporation method at different drug:polymer ratios (w/w). BZL-PEG 6000 formulations were characterized by X-ray diffraction (XRD), scanning electron microscopy, and dissolution studies. The preparation of SD-based BZL tablets by the wet granulation method was carried out and the influence of pregelatinized starch (PS) and starch (S) on the disintegration time and drug dissolution rate was analyzed. SDs showed a significant improvement in the release profile of BZL as compared with the pure drug. As demonstrated by XRD, the crystalline character of BZL remained almost unaltered in both PMs and SDs. BZL release from the PEG 6000 tablets increased by the presence of PS instead S. Unexpectedly, the BZL release from tablets containing PMs was almost equal as compared with the BZL release from tablets containing SDs. In conclusion, the results suggest that PEG 6000 and PS are suitable additives for the development of BZL tablets with enhanced dissolution behavior through the preparation of ordinary PMs, instead the laborious SDs.

BDDCS class prediction for new molecular entities

The Biopharmaceutics Drug Disposition Classification System (BDDCS) was successfully employed for predicting drug-drug interactions (DDIs) with respect to drug metabolizing enzymes (DMEs), drug transporters and their interplay. The major assumption of BDDCS is that the extent of metabolism (EoM) predicts high versus low intestinal permeability rate, and vice versa, at least when uptake transporters or paracellular transport is not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated with in vitro assays, could anticipate disposition and potential DDIs of new molecular entities (NMEs). Here we describe a computational procedure for predicting BDDCS class from molecular structures. The model was trained on a set of 300 oral drugs, and validated on an external set of 379 oral drugs, using 17 descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction the accuracy was 82% in training and 79% in external validation. The actual BDDCS class corresponded to the highest ranked calculated class for 55% of the validation molecules, and it was within the top two ranked more than 92% of the time. The unbalanced stratification of the data set did not affect the prediction, which showed highest accuracy in predicting classes 2 and 3 with respect to the most populated class 1. For class 4 drugs a general lack of predictability was observed. A linear discriminant analysis (LDA) confirming the degree of accuracy for the prediction of the different BDDCS classes is tied to the structure of the data set. This model could routinely be used in early drug discovery to prioritize in vitro tests for NMEs (e.g., affinity to transporters, intestinal metabolism, intestinal absorption and plasma protein binding). We further applied the BDDCS prediction model on a large set of medicinal chemistry compounds (over 30,000 chemicals). Based on this application, we suggest that solubility, and not permeability, is the major difference between NMEs and drugs. We anticipate that the forecast of BDDCS categories in early drug discovery may lead to a significant R&D cost reduction.

From Molecular Docking to 3D-Quantitative Structure-Activity Relationships (3D-QSAR): Insights into the Binding Mode of 5-Lipoxygenase Inhibitors

Pharmacological intervention with 5-Lipoxygenase (5-LO) is a promising strategy for treatment of inflammatory and allergic ailments, including asthma. With the aim of developing predictive models of 5-LO affinity and gaining insights into the molecular basis of ligand-target interaction, we herein describe QSAR studies of 59 diverse nonredox-competitive 5-LO inhibitors based on the use of molecular shape descriptors and docking experiments. These studies have successfully yielded a predictive model able to explain much of the variance in the activity of the training set compounds while predicting satisfactorily the 5-LO inhibitory activity of an external test set of compounds. The inspection of the selected variables in the QSAR equation unveils the importance of specific interactions which are observed from docking experiments. Collectively, these results may be used to design novel potent and selective nonredox 5-LO inhibitors.

BDDCS applied to over 900 drugs

Here, we compile the Biopharmaceutics Drug Disposition Classification System (BDDCS) classification for 927 drugs, which include 30 active metabolites. Of the 897 parent drugs, 78.8% (707) are administered orally. Where the lowest measured solubility is found, this value is reported for 72.7% (513) of these orally administered drugs and a dose number is recorded. The measured values are reported for percent excreted unchanged in urine, LogP, and LogD (7.4) when available. For all 927 compounds, the in silico parameters for predicted Log solubility in water, calculated LogP, polar surface area, and the number of hydrogen bond acceptors and hydrogen bond donors for the active moiety are also provided, thereby allowing comparison analyses for both in silico and experimentally measured values. We discuss the potential use of BDDCS to estimate the disposition characteristics of novel chemicals (new molecular entities) in the early stages of drug discovery and development. Transporter effects in the intestine and the liver are not clinically relevant for BDDCS class 1 drugs, but potentially can have a high impact for class 2 (efflux in the gut, and efflux and uptake in the liver) and class 3 (uptake and efflux in both gut and liver) drugs. A combination of high dose and low solubility is likely to cause BDDCS class 4 to be underpopulated in terms of approved drugs (N = 53 compared with over 200 each in classes 1-3). The influence of several measured and in silico parameters in the process of BDDCS category assignment is discussed in detail.

Identification and validation of novel human pregnane X receptor activators among prescribed drugs via ligand-based virtual screening

Human pregnane X receptor (hPXR) plays a key role in regulating metabolism and clearance of endogenous and exogenous substances. Identification of novel hPXR activators among commercial drugs may aid in avoiding drug-drug interactions during coadministration. We applied ligand-based computational approaches for virtual screening of a commonly prescribed drug database (SCUT). Bayesian classification models were generated with a training set comprising 177 compounds using Fingerprints and 117 structural descriptors. A cell-based luciferase reporter assay was used for evaluation of chemical-mediated hPXR activation in HepG2 cells. All compounds were tested at 10 μM concentration with rifampicin and dimethyl sulfoxide as positive and negative controls, respectively. The Bayesian models showed specificity and overall prediction accuracy up to 0.92 and 0.69 for test set compounds. Screening the SCUT database with this model retrieved 105 hits and 17 compounds from the top 25 hits were chosen for in vitro testing. The reporter assay confirmed that nine drugs, i.e., fluticasone, nimodipine, nisoldipine, beclomethasone, finasteride, flunisolide, megestrol, secobarbital, and aminoglutethimide, were previously unidentified hPXR activators. Thus, the present study demonstrates that novel hPXR activators can be efficiently identified among U.S. Food and Drug Administration-approved and commonly prescribed drugs, which should lead to detection and prevention of potential drug-drug interactions.

Impact of Biopharmaceutics Classification System-based biowaivers

The Biopharmaceutics Classification System (BCS) is employed to waive in vivo bioequivalence testing (i.e. provide "biowaivers") for new and generic drugs that are BCS class I. Granting biowaivers under systems such as the BCS eliminates unnecessary drug exposures to healthy subjects and provides economic relief, while maintaining the high public health standard for therapeutic equivalence. International scientific consensus suggests class III drugs are also eligible for biowaivers. The objective of this study was to estimate the economic impact of class I BCS-based biowaivers, along with the economic impact of a potential expansion to BCS class III. Methods consider the distribution of drugs across the four BCS classes, numbers of in vivo bioequivalence studies performed from a five year period, and effects of highly variable drugs (HVDs). Results indicate that 26% of all drugs are class I non-HVDs, 7% are class I HVDs, 27% are class III non-HVDs, and 3% are class III HVDs. An estimated 66 to 76 million dollars can be saved each year in clinical study costs if all class I compounds were granted biowaivers. Between 21 and 24 million dollars of this savings is from HVDs. If BCS class III compounds were also granted waivers, an additional direct savings of 62 to 71 million dollars would be realized, with 9 to 10 million dollars coming from HVDs.

Targeting drug transporters - combining in silico and in vitro approaches to predict in vivo

Transporter proteins are expressed throughout the human body in different vital organs. They play an important role to various extents in determining absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties of therapeutic molecules. Over the past decade, numerous drug transporters have been cloned and considerable progress has been made toward understanding the molecular characteristics of individual transporters. In this chapter several in vitro and in silico techniques are described with applications to understand transporter behavior. These include employing new techniques to rapidly identify novel ligands for transporters. Ultimately these methods should lead to a greater overall appreciation of the role of transporters in vivo.

Precompetitive preclinical ADME/Tox data: set it free on the web to facilitate computational model building and assist drug development

Web-based technologies coupled with a drive for improved communication between scientists have resulted in the proliferation of scientific opinion, data and knowledge at an ever-increasing rate. The increasing array of chemistry-related computer-based resources now available provides chemists with a direct path to the discovery of information, once previously accessed via library services and limited to commercial and costly resources. We propose that preclinical absorption, distribution, metabolism, excretion and toxicity data as well as pharmacokinetic properties from studies published in the literature (which use animal or human tissues in vitro or from in vivo studies) are precompetitive in nature and should be freely available on the web. This could be made possible by curating the literature and patents, data donations from pharmaceutical companies and by expanding the currently freely available ChemSpider database of over 21 million molecules with physicochemical properties. This will require linkage to PubMed, PubChem and Wikipedia as well as other frequently used public databases that are currently used, mining the full text publications to extract the pertinent experimental data. These data will need to be extracted using automated and manual methods, cleaned and then published to the ChemSpider or other database such that it will be freely available to the biomedical research and clinical communities. The value of the data being accessible will improve development of drug molecules with good ADME/Tox properties, facilitate computational model building for these properties and enable researchers to not repeat the failures of past drug discovery studies.

Principles for 3D/4D QSAR classification of drugs

The principles for the 3D/4D classification of drugs are introduced in this article. Based on these principles, new techniques for the reconstruction of complementary selfconsistent receptor fields for the classification of drugs, taking into account their multitautomeric and multiconformational states, are created. The series of examples of classification of drugs by their activity (active or nonactive), by their mechanisms of action, by their target and binding site and by the most important stages of their action are given. Prospects for rational drug design are highlighted.

Machine learning methods and docking for predicting human pregnane X receptor activation

The pregnane X receptor (PXR) regulates the expression of genes involved in xenobiotic metabolism and transport. In vitro methods to screen for PXR agonists are used widely. In the current study, computational models for human PXR activators and PXR nonactivators were developed using recursive partitioning (RP), random forest (RF), and support vector machine (SVM) algorithms with VolSurf descriptors. Following 10-fold randomization, the models correctly predicted 82.6-98.9% of activators and 62.0-88.6% of nonactivators. The models were validated using separate test sets. The overall ( n = 15) test set prediction accuracy for PXR activators with RP, RF, and SVM PXR models is 80-93.3%, representing an improvement over models previously reported. All models were tested with a second test set ( n = 145), and the prediction accuracy ranged from 63 to 67% overall. These test set molecules were found to cover the same area in a principal component analysis plot as the training set, suggesting that the predictions were within the applicability domain. The FlexX docking method combined with logistic regression performed poorly in classifying this PXR test set as compared with RP, RF, and SVM but may be useful for qualitative interpretion of interactions within the LBD. From this analysis, VolSurf descriptors and machine learning methods had good classification accuracy and made reliable predictions within the model applicability domain. These methods could be used for high throughput virtual screening to assess for PXR activation, prior to in vitro testing to predict potential drug-drug interactions.

In vitro studies are sometimes better than conventional human pharmacokinetic in vivo studies in assessing bioequivalence of immediate-release solid oral dosage forms

Human pharmacokinetic in vivo studies are often presumed to serve as the "gold standard" to assess product bioequivalence (BE) of immediate-release (IR) solid oral dosage forms. However, when this general assumption is re-visited, it appears that in vitro studies are sometimes better than in vivo studies in assessing BE of IR solid oral dosage forms. Reasons for in vitro studies to sometimes serve as the better method are that in vitro studies: (a) reduce costs, (b) more directly assess product performance, and (c) offer benefits in terms of ethical considerations. Reduced costs are achieved through avoiding in vivo studies where BE is self-evident, where biopharmaceutic data anticipates BE, and where in vivo BE study type II error is high. In vitro studies more directly assess product performance than do conventional human pharmacokinetic BE studies, since in vitro studies focus on comparative drug absorption from the two products, while in vivo BE testing can suffer from complications due to its indirect approach. Regarding ethical considerations, in vitro studies better embrace the principle "No unnecessary human testing should be performed" and can result in faster development. Situations when in vitro test should be viewed as preferred include Class I drugs with rapid dissolution, Class III drugs with very rapid dissolution, and highly variable drugs with rapid dissolution and that are not bio(equivalence)problem drugs. Sponsors of potential in vivo human pharmacokinetic BE testing should be required to justify why in vitro data is insufficient, similar to proposed animal testing requires justification to not employ an in vitro approach.