In silico prediction of volume of distribution in human using linear and nonlinear models on a 669 compound data set

AI-Powered Mining of Highly Customized and Superior ESIPT-Based Fluorescent Probes

Excited-state intramolecular proton transfer (ESIPT) has attracted great attention in fluorescent sensors and luminescent materials due to its unique photobiological and photochemical features. However, the current structures are far from meeting the specific demands for ESIPT molecules in different scenarios; the try-and-error development method is labor-intensive and costly. Therefore, it is imperative to devise novel approaches for the exploration of promising ESIPT fluorophores. This research proposes an artificial intelligence approach aiming at exploring ESIPT molecules efficiently. The first high-quality ESIPT dataset and a multi-level prediction system are constructed that realized accurate identification of ESIPT molecules from a large number of compounds under a stepwise distinguishing from conventional molecules to fluorescent molecules and then to ESIPT molecules. Furthermore, key structural features that contributed to ESIPT are revealed by using the SHapley Additive exPlanations (SHAP) method. Then three strategies are proposed to ensure the ESIPT process while keeping good safety, pharmacokinetic properties, and novel structures. With these strategies, >700 previously unreported ESIPT molecules are screened from a large pool of 570 000 compounds. The ESIPT process and biosafety of optimal molecules are successfully validated by quantitative calculation and experiment. This novel approach is expected to bring a new paradigm for exploring ideal ESIPT molecules.

Introducing enzymatic cleavage features and transfer learning realizes accurate peptide half-life prediction across species and organs

Peptide drugs are becoming star drug agents with high efficiency and selectivity which open up new therapeutic avenues for various diseases. However, the sensitivity to hydrolase and the relatively short half-life have severely hindered their development. In this study, a new generation artificial intelligence-based system for accurate prediction of peptide half-life was proposed, which realized the half-life prediction of both natural and modified peptides and successfully bridged the evaluation possibility between two important species (human, mouse) and two organs (blood, intestine). To achieve this, enzymatic cleavage descriptors were integrated with traditional peptide descriptors to construct a better representation. Then, robust models with accurate performance were established by comparing traditional machine learning and transfer learning, systematically. Results indicated that enzymatic cleavage features could certainly enhance model performance. The deep learning model integrating transfer learning significantly improved predictive accuracy, achieving remarkable R2 values: 0.84 for natural peptides and 0.90 for modified peptides in human blood, 0.984 for natural peptides and 0.93 for modified peptides in mouse blood, and 0.94 for modified peptides in mouse intestine on the test set, respectively. These models not only successfully composed the above-mentioned system but also improved by approximately 15% in terms of correlation compared to related works. This study is expected to provide powerful solutions for peptide half-life evaluation and boost peptide drug development.

The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery

Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.

Technological advances in the use of viral and non-viral vectors for delivering genetic and non-genetic cargos for cancer therapy

The burden of cancer is increasing globally. Several challenges facing its mainstream treatment approaches have formed the basis for the development of targeted delivery systems to carry and distribute anti-cancer payloads to their defined targets. This site-specific delivery of drug molecules and gene payloads to selectively target druggable biomarkers aimed at inducing cell death while sparing normal cells is the principal goal for cancer therapy. An important advantage of a delivery vector either viral or non-viral is the cumulative ability to penetrate the haphazardly arranged and immunosuppressive tumour microenvironment of solid tumours and or withstand antibody-mediated immune response. Biotechnological approaches incorporating rational protein engineering for the development of targeted delivery systems which may serve as vehicles for packaging and distribution of anti-cancer agents to selectively target and kill cancer cells are highly desired. Over the years, these chemically and genetically modified delivery systems have aimed at distribution and selective accumulation of drug molecules at receptor sites resulting in constant maintenance of high drug bioavailability for effective anti-tumour activity. In this review, we highlighted the state-of-the art viral and non-viral drug and gene delivery systems and those under developments focusing on cancer therapy.

Lipophilic Studies and In Silico ADME Profiling of Biologically Active 2-Aminothiazol-4(5H)-one Derivatives

Pseudothiohydantoin derivatives have a wide range of biological activities and are widely used in the development of new pharmaceuticals. Lipophilicity is a basic, but very important parameter in the design of potential drugs, as it determines solubility in lipids, nonpolar solvents, and makes it possible to predict the ADME profile. The aim of this study was to evaluate the lipophilicity of 28 pseudothiohydantoin derivatives showing the inhibition of 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) using chromatographic methods. Experimentally, lipophilicity was determined by reverse phase thin layer chromatography (RP-TLC) and reverse phase high-performance liquid chromatography (RP-HPLC). In both methods, methanol was used as the organic modifier of the mobile phase. For each 2-aminothiazol-4(5H)-one derivative, a relationship was observed between the structure of the compound and the values of the lipophilicity parameters (log kw, RM0). Experimental lipophilicity values were compared with computer calculated partition coefficient (logP) values. A total of 27 of the 28 tested compounds had a lipophilicity value < 5, which therefore met the condition of Lipinski's rule. In addition, the in silico ADME assay showed favorable absorption, distribution, metabolism, and excretion parameters for most of the pseudothiohydantoin derivatives tested. The study of lipophilicity and the ADME analysis indicate that the tested compounds are good potential drug candidates.

Evaluation of Prediction Accuracy for Volume of Distribution in Rat and Human Using In Vitro, In Vivo, PBPK and QSAR Methods

Volume of distribution at steady state (V_ss) is an important pharmacokinetic parameter of a drug candidate. In this study, V_ss prediction accuracy was evaluated by using: (1) seven methods for rat with 56 compounds, (2) four methods for human with 1276 compounds, and (3) four in vivo methods and three Kp (partition coefficient) scalar methods from scaling of three preclinical species with 125 compounds. The results showed that the global QSAR models outperformed the PBPK methods. Tissue fraction unbound (f_u,t) method with adipose and muscle also provided high V_ss prediction accuracy. Overall, the high performing methods for human V_ss prediction are the global QSAR models, Øie-Tozer and equivalency methods from scaling of preclinical species, as well as PBPK methods with Kp scalar from preclinical species. Certain input parameter ranges rendered PBPK models inaccurate due to mass balance issues. These were addressed using appropriate theoretical limit checks. Prediction accuracy of tissue Kp were also examined. The f_u,t method predicted Kp values more accurately than the PBPK methods for adipose, heart and muscle. All the methods overpredicted brain Kp and underpredicted liver Kp due to transporter effects. Successful V_ss prediction involves strategic integration of in silico, in vitro and in vivo approaches.

In Silico Models of Human PK Parameters. Prediction of Volume of Distribution Using an Extensive Data Set and a Reduced Number of Parameters

A novel, descriptor-parsimonious in silico model to predict human VDss (volume of distribution at steady-state) has been derived and thoroughly tested in a quasi-prospective regimen using an independent test set of 213 compounds. The model performs on par with a former benchmark model that relied on far more descriptors. As a result, the new random forest model relying on only six descriptors allows for interpretations that help chemists to design compounds with desired human VDss values. A comparison of in silico predictions of VDss with models using in vitro derived descriptors or in vivo scaling methods supports the strength of the in-silico approach, considering its resource- and animal-sparing nature. The strong performance of the in silico VDss models on structurally novel compounds supports the high degree of confidence that can be placed in using in silico human VDss predictions for compound design and human dose predictions.

Improving the Accuracy of Predicted Human Pharmacokinetics: Lessons Learned from the AstraZeneca Drug Pipeline Over Two Decades

During drug discovery and prior to the first human dose of a novel candidate drug, the pharmacokinetic (PK) behavior of the drug in humans is predicted from preclinical data. This helps to inform the likelihood of achieving therapeutic exposures in early clinical development. Once clinical data are available, the observed human PK are compared with predictions, providing an opportunity to assess and refine prediction methods. Application of best practice in experimental data generation and predictive methodologies, and a focus on robust mechanistic understanding of the candidate drug disposition properties before nomination to clinical development, have led to maximizing the probability of successful PK predictions so that 83% of AstraZeneca drug development projects progress in the clinic with no PK issues; and 71% of key PK parameter predictions [64% of area under the curve (AUC) predictions; 78% of maximum concentration (C_max) predictions; and 70% of half-life predictions] are accurate to within twofold. Here, we discuss methods to predict human PK used by AstraZeneca, how these predictions are assessed and what can be learned from evaluating the predictions for 116 candidate drugs.

ADMET profiling of geographically diverse phytochemical using chemoinformatic tools

Aim: Phytocompounds are important due to their uniqueness, however, only few reach the development phase due to their poor pharmacokinetics. Therefore, preassessing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties is essential in drug discovery. Methodology: Biologically diverse databases (Phytochemica, SerpentinaDB, SANCDB and NuBBE_DB) covering the region of India, Brazil and South Africa were considered to predict the ADMET using chemoinformatic tools (Qikprop, pkCSM and DataWarrior). Results: Screening through each of pharmacokinetic criteria resulted in identification of 24 compounds that adhere to all the ADMET properties. Furthermore, assessment revealed that five have potent anticancer biological activity against cancer cell lines. Conclusion: We have established an open-access database (ADMET-BIS) to enable identification of promising molecules that follow ADMET properties and can be considered for drug development.

Molecular Modeling Approaches for the Prediction of Selected Pharmacokinetic Properties

Poor profiles of potential drug candidates, including pharmacokinetic properties, have been acknowledged as a significant hindrance to the development of modern therapeutics. Contemporary drug discovery and development would be incomplete without the aid of molecular modeling (in-silico) techniques, allowing the prediction of pharmacokinetic properties such as clearance, unbound fraction, volume of distribution and bioavailability. As with all models, in-silico approaches are subject to their interpretability, a trait that must be balanced with accuracy when considering the development of new methods. The best models will always require reliable data to inform them, presenting significant challenges, particularly when appropriate in-vitro or in-vivo data may be difficult or time-consuming to obtain. This article seeks to review some of the key in-silico techniques used to predict key pharmacokinetic properties and give commentary on the current and future directions of the field.

An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction

BACKGROUND

Pharmacokinetic evaluation is one of the key processes in drug discovery and development. However, current absorption, distribution, metabolism, and excretion prediction models still have limited accuracy.

AIM

This study aims to construct an integrated transfer learning and multitask learning approach for developing quantitative structure-activity relationship models to predict four human pharmacokinetic parameters.

METHODS

A pharmacokinetic data set included 1104 U.S. FDA approved small molecule drugs. The data set included four human pharmacokinetic parameter subsets (oral bioavailability, plasma protein binding rate, apparent volume of distribution at steady-state, and elimination half-life). The pretrained model was trained on over 30 million bioactivity data entries. An integrated transfer learning and multitask learning approach was established to enhance the model generalization.

RESULTS

The pharmacokinetic data set was split into three parts (60:20:20) for training, validation, and testing by the improved maximum dissimilarity algorithm with the representative initial set selection algorithm and the weighted distance function. The multitask learning techniques enhanced the model predictive ability. The integrated transfer learning and multitask learning model demonstrated the best accuracies, because deep neural networks have the general feature extraction ability; transfer learning and multitask learning improve the model generalization.

CONCLUSIONS

The integrated transfer learning and multitask learning approach with the improved data set splitting algorithm was first introduced to predict the pharmacokinetic parameters. This method can be further employed in drug discovery and development.

Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 1352 Drug Compounds

We report a trend analysis of human intravenous pharmacokinetic data on a data set of 1352 drugs. The aim in building this data set and its detailed analysis was to provide, as in the previous case published in 2008, an extended, robust, and accurate resource that could be applied by drug metabolism, clinical pharmacology, and medicinal chemistry scientists to a variety of scaling approaches. All in vivo data were obtained or derived from original references, either through the literature or regulatory agency reports, exclusively from studies utilizing intravenous administration. Plasma protein binding data were collected from other available sources to supplement these pharmacokinetic data. These parameters were analyzed concurrently with a range of physicochemical properties, and resultant trends and patterns within the data are presented. In addition, the date of first disclosure of each molecule was reported and the potential "temporal" impact on data trends was analyzed. The findings reported here are consistent with earlier described trends between pharmacokinetic behavior and physicochemical properties. Furthermore, the availability of a large data set of pharmacokinetic data in humans will be important to further pursue analyses of physicochemical properties, trends, and modeling efforts and should propel our deeper understanding (especially in terms of clearance) of the absorption, distribution, metabolism, and excretion behavior of drug compounds.

Evaluating the influence of half-life, milk:plasma partition coefficient, and volume of distribution on lactational exposure to chemicals in children

Women are exposed to multiple environmental chemicals, many of which are known to transfer to breast milk during lactation. However, little is known about the influence of the different chemical-specific pharmacokinetic parameters on children's lactational dose. Our objective was to develop a generic pharmacokinetic model and subsequently quantify the influence of three chemical-specific parameters (biological half-life, milk:plasma partition coefficient, and volume of distribution) on lactational exposure to chemicals and resulting plasma levels in children. We developed a two-compartment pharmacokinetic model to simulate lifetime maternal exposure, placental transfer, and lactational exposure to the child. We performed 10,000 Monte Carlo simulations where half-life, milk:plasma partition coefficient, and volume of distribution were varied. Children's dose and plasma levels were compared to their mother's by calculating child:mother dose ratios and plasma level ratios. We then evaluated the association between the three chemical-specific pharmacokinetic parameters and child:mother dose and level ratios through linear regression and decision trees. Our analyses revealed that half-life was the most influential parameter on children's lactational dose and plasma concentrations, followed by milk:plasma partition coefficient and volume of distribution. In bivariate regression analyses, half-life explained 72% of child:mother dose ratios and 53% of child:mother level ratios. Decision trees aiming to identify chemicals with high potential for lactational exposure (ratio>1) had an accuracy of 89% for child:mother dose ratios and 84% for child:mother level ratios. Our study showed the relative importance of half-life, milk:plasma partition coefficient, and volume of distribution on children's lactational exposure. Developed equations and decision trees will enable the rapid identification of chemicals with a high potential for lactational exposure.

Drug Distribution Part 2. Predicting Volume of Distribution from Plasma Protein Binding and Membrane Partitioning

PURPOSE

Volume of distribution is an important pharmacokinetic parameter in the distribution and half-life of a drug. Protein binding and lipid partitioning together determine drug distribution.

METHODS

Here we present a simple relationship that estimates the volume of distribution with the fraction of drug unbound in both plasma and microsomes. Model equations are based upon a two-compartment system and the experimental fractions unbound in plasma and microsomes represent binding to plasma proteins and cellular lipids, respectively.

RESULTS

The protein and lipid binding components were parameterized using a dataset containing human in vitro and in vivo parameters for 63 drugs. The resulting equation explains ~84% of the variance in the log of the volume of distribution with an average fold-error of 1.6, with 3 outliers.

CONCLUSIONS

These results suggest that V_ss can be predicted for most drugs from plasma protein binding and microsomal partitioning.

Systematic Evaluation of Wajima Superposition (Steady-State Concentration to Mean Residence Time) in the Estimation of Human Intravenous Pharmacokinetic Profile

We present a systematic evaluation of the Wajima superpositioning method to estimate the human intravenous (i.v.) pharmacokinetic (PK) profile based on a set of 54 marketed drugs with diverse structure and range of physicochemical properties. We illustrate the use of average of "best methods" for the prediction of clearance (CL) and volume of distribution at steady state (VDss) as described in our earlier work (Lombardo F, Waters NJ, Argikar UA, et al. J Clin Pharmacol. 2013;53(2):178-191; Lombardo F, Waters NJ, Argikar UA, et al. J Clin Pharmacol. 2013;53(2):167-177). These methods provided much more accurate prediction of human PK parameters, yielding 88% and 70% of the prediction within 2-fold error for VDss and CL, respectively. The prediction of human i.v. profile using Wajima superpositioning of rat, dog, and monkey time-concentration profiles was tested against the observed human i.v. PK using fold error statistics. The results showed that 63% of the compounds yielded a geometric mean of fold error below 2-fold, and an additional 19% yielded a geometric mean of fold error between 2- and 3-fold, leaving only 18% of the compounds with a relatively poor prediction. Our results showed that good superposition was observed in any case, demonstrating the predictive value of the Wajima approach, and that the cause of poor prediction of human i.v. profile was mainly due to the poorly predicted CL value, while VDss prediction had a minor impact on the accuracy of human i.v. profile prediction.

pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures

Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (http://structure.bioc.cam.ac.uk/pkcsm), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

BACKGROUND

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values.

RESULTS

Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied.

CONCLUSIONS

Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Development of a consumer product ingredient database for chemical exposure screening and prioritization

Consumer products are a primary source of chemical exposures, yet little structured information is available on the chemical ingredients of these products and the concentrations at which ingredients are present. To address this data gap, we created a database of chemicals in consumer products using product Material Safety Data Sheets (MSDSs) publicly provided by a large retailer. The resulting database represents 1797 unique chemicals mapped to 8921 consumer products and a hierarchy of 353 consumer product "use categories" within a total of 15 top-level categories. We examine the utility of this database and discuss ways in which it will support (i) exposure screening and prioritization, (ii) generic or framework formulations for several indoor/consumer product exposure modeling initiatives, (iii) candidate chemical selection for monitoring near field exposure from proximal sources, and (iv) as activity tracers or ubiquitous exposure sources using "chemical space" map analyses. Chemicals present at high concentrations and across multiple consumer products and use categories that hold high exposure potential are identified. Our database is publicly available to serve regulators, retailers, manufacturers, and the public for predictive screening of chemicals in new and existing consumer products on the basis of exposure and risk.

Dabigatran, intracranial hemorrhage, and the neurosurgeon

Dabigatran etexilate (Pradaxa) is a novel oral anticoagulant that has gained FDA approval for the prevention of ischemic stroke and systemic embolism in patients with nonvalvular atrial fibrillation. In randomized trials, the incidence of hemorrhagic events has been demonstrated to be lower in patients treated with dabigatran compared with the traditional anticoagulant warfarin. However, dabigatran does not have reliable laboratory tests to measure levels of anticoagulation and there is no pharmacological antidote. These drawbacks are challenging in the setting of intracerebral hemorrhage. In this article, the authors provide background information on dabigatran, review the existing anecdotal experiences with treating intracerebral hemorrhage related to dabigatran therapy, present a case study of intracranial hemorrhage in a patient being treated with dabigatran, and suggest clinical management strategies. The development of reversal agents is urgently needed given the growing number of patients treated with this medication.

Applying linear and non-linear methods for parallel prediction of volume of distribution and fraction of unbound drug

Volume of distribution and fraction unbound are two key parameters in pharmacokinetics. The fraction unbound describes the portion of free drug in plasma that may extravasate, while volume of distribution describes the tissue access and binding of a drug. Reliable in silico predictions of these pharmacokinetic parameters would benefit the early stages of drug discovery, as experimental measuring is not feasible for screening purposes. We have applied linear and nonlinear multivariate approaches to predict these parameters: linear partial least square regression and non-linear recursive partitioning classification. The volume of distribution and fraction of unbound drug in plasma are predicted in parallel within the model, since the two are expected to be affected by similar physicochemical drug properties. Predictive models for both parameters were built and the performance of the linear models compared to models included in the commercial software Volsurf+. Our models performed better in predicting the unbound fraction (Q² 0.54 for test set compared to 0.38 with Volsurf+ model), but prediction accuracy of the volume of distribution was comparable to the Volsurf+ model (Q² of 0.70 for test set compared to 0.71 with Volsurf+ model). The nonlinear classification models were able to identify compounds with a high or low volume of distribution (sensitivity 0.81 and 0.71, respectively, for test set), while classification of fraction unbound was less successful. The interrelationship between the volume of distribution and fraction unbound is investigated and described in terms of physicochemical descriptors. Lipophilicity and solubility descriptors were found to have a high influence on both volume of distribution and fraction unbound, but with an inverse relationship.

A perspective on the prediction of drug pharmacokinetics and disposition in drug research and development

Prediction of human pharmacokinetics of new drugs, as well as other disposition attributes, has become a routine practice in drug research and development. Prior to the 1990s, drug disposition science was used in a mostly descriptive manner in the drug development phase. With the advent of in vitro methods and availability of human-derived reagents for in vitro studies, drug-disposition scientists became engaged in the compound design phase of drug discovery to optimize and predict human disposition properties prior to nomination of candidate compounds into the drug development phase. This has reaped benefits in that the attrition rate of new drug candidates in drug development for reasons of unacceptable pharmacokinetics has greatly decreased. Attributes that are predicted include clearance, volume of distribution, half-life, absorption, and drug-drug interactions. In this article, we offer our experience-based perspectives on the tools and methods of predicting human drug disposition using in vitro and animal data.

Comprehensive assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data, part 1: volume of distribution at steady state

The authors present a comprehensive analysis on the estimation of volume of distribution at steady state (VD(ss) ) in human based on rat, dog, and monkey data on nearly 400 compounds for which there are also associated human data. This data set, to the authors- knowledge, is the largest publicly available, has been carefully compiled from literature reports, and was expanded with some in-house determinations such as plasma protein binding data. This work offers a good statistical basis for the evaluation of applicable prediction methods, their accuracy, and some methods-dependent diagnostic tools. The authors also grouped the compounds according to their charge classes and show the applicability of each method considered to each class, offering further insight into the probability of a successful prediction. Furthermore, they found that the use of fraction unbound in plasma, to obtain unbound volume of distribution, is generally detrimental to accuracy of several methods, and they discuss possible reasons. Overall, the approach using dog and monkey data in the íie-Tozer equation offers the highest probability of success, with an intrinsic diagnostic tool based on aberrant values (<0 or >1) for the calculated fraction unbound in tissue. Alternatively, methods based on dog data (single-species scaling) and rat and dog data (íie-Tozer equation with 2 species or multiple regression methods) may be considered reasonable approaches while not requiring data in nonhuman primates.

Strategies for the generation, validation and application of in silico ADMET models in lead generation and optimization

INTRODUCTION

The most desirable chemical starting point in drug discovery is a hit or lead with a good overall profile, and where there may be issues; a clear SAR strategy should be identifiable to minimize the issue. Filtering based on drug-likeness concepts are a first step, but more accurate theoretical methods are needed to i) estimate the biological profile of molecule in question and ii) based on the underlying structure-activity relationships used by the model, estimate whether it is likely that the molecule in question can be altered to remove these liabilities.

AREAS COVERED

In this paper, the authors discuss the generation of ADMET models and their practical use in decision making. They discuss the issues surrounding data collation, experimental errors, the model assessment and validation steps, as well as the different types of descriptors and statistical models that can be used. This is followed by a discussion on how the model accuracy will dictate when and where it can be used in the drug discovery process. The authors also discuss how models can be developed to more effectively enable multiple parameter optimization.

EXPERT OPINION

Models can be applied in lead generation and lead optimization steps to i) rank order a collection of hits, ii) prioritize the experimental assays needed for different hit series, iii) assess the likelihood of resolving a problem that might be present in a particular series in lead optimization and iv) screen a virtual library based on a hit or lead series to assess the impact of diverse structural changes on the predicted properties.

Getting the MAX out of Computational Models: The Prediction of Unbound-Brain and Unbound-Plasma Maximum Concentrations

The objective of this work was to establish that unbound maximum concentrations may be reasonably predicted from a combination of computed molecular properties assuming subcutaneous (SQ) dosing. Additionally, we show that the maximum unbound plasma and brain concentrations may be projected from a mixture of in vitro absorption, distribution, metabolism, excretion experimental parameters in combination with computed properties (volume of distribution, fraction unbound in microsomes). Finally, we demonstrate the utility of the underlying equations by showing that the maximum total plasma concentrations can be projected from the experimental parameters for a set of compounds with data collected from clinical research.

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.

Prediction of pharmacokinetic parameters

In silico tools specifically developed for prediction of pharmacokinetic parameters are of particular interest to pharmaceutical industry because of the high potential of discarding inappropriate molecules during an early stage of drug development itself with consequent saving of vital resources and valuable time. The ultimate goal of the in silico models of absorption, distribution, metabolism, and excretion (ADME) properties is the accurate prediction of the in vivo pharmacokinetics of a potential drug molecule in man, whilst it exists only as a virtual structure. Various types of in silico models developed for successful prediction of the ADME parameters like oral absorption, bioavailability, plasma protein binding, tissue distribution, clearance, half-life, etc. have been briefly described in this chapter.

Prediction of steady-state volume of distribution of acidic drugs by quantitative structure-pharmacokinetics relationships

The volume of distribution (VD) is one of the most important pharmacokinetic parameters of drugs. The present study employs quantitative structure-pharmacokinetics relationships (QSPkR) to derive models for VD prediction of acidic drugs. The steady-state volume of distribution (VD(ss)) values of 132 acidic drugs were collected, the chemical structures were described by 178 molecular descriptors, and QSPkR models were derived after variable selection by genetic algorithm and stepwise regression. Models were validated by cross-validation procedures and external test set. According to the molecular descriptors selected as the most predictive for VD(ss), the presence of seven- and nine-member cycles, atom type P(5+), SH groups, and large nonionized substituents increase the VD(ss), whereas atom types S(2+) and S(4+) and polar ionized substituents decrease it. Cross-validation and external validation studies on the QSPkR models derived in the present study showed good predictive ability with mean fold error values ranging from 1.58 (cross-validation) to 2.25 (external validation). The model performance is comparable to more complicated methods requiring in vitro or in vivo experiments and superior to the existing QSPkR models concerning acidic drugs. Apart from the prediction of VD in human, present models are also useful as a curator of available pharmacokinetic databases.