Hologram QSAR model for the prediction of human oral bioavailability

Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models

Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug's physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field.

Prediction of the First-Pass Metabolism of a Drug After Oral Intake Based on Structural Parameters and Physicochemical Properties

BACKGROUND AND OBJECTIVE

The oral first-pass metabolism is a crucial factor that plays a key role in a drug's pharmacokinetic profile. Prediction of the oral first-pass metabolism based on chemical structural parameters can be useful in the drug-design process. Developing an orally administered drug with an acceptable pharmacokinetic profile is necessary to reduce the cost and time associated with evaluating the extent of the first-pass metabolism of a candidate compound in preclinical studies. The aim of this study is to estimate the first-pass metabolism of an orally administered drug.

METHODS

A set of compounds with reported first-pass metabolism data were collected. Moreover, human intestinal absorption percentage and oral bioavailability data were extracted from the literature to propose a classification system that split the drugs up based on their first-pass metabolism extents. Various structural parameters were calculated for each compound. The relations of the structural and physicochemical values of each compound to the class the compound belongs to were obtained using logistic regression.

RESULTS

Initial analysis showed that compounds with logD7.4 > 1 or a rugosity factor of > 1.5 are more likely to have high first-pass metabolism. Four different models that can predict the oral first-pass metabolism with acceptable error were introduced. The overall accuracies of the models were in the range of 72% (for models with simple descriptors) to 78% (for models with complex descriptors). Although the models with simple descriptors have lower accuracies compared to complex models, they are more interpretable and easier for researchers to utilize.

CONCLUSION

A novel classification of drugs based on the extent of the oral first-pass metabolism was introduced, and mechanistic models were developed to assign candidate compounds to the appropriate proposed classes.

Binding mechanism of pentamidine derivatives with human serum acute phase protein α1-acid glycoprotein

Drug binding and interactions with plasma proteins play a crucial role in determining the efficacy of drug delivery, thus significantly impacting the overall pharmacological effect. AGP, the second most abundant plasma protein in blood circulation, has the unique capability to bind drugs and transport various compounds. In our present study, for the first time, we investigated whether AGP, a major component of the acute phase lipocalin in human plasma, can bind with pentamidine derivatives known for their high activity against the fungal pathogen Pneumocystis carinii. This investigation was conducted using integrated spectroscopic techniques and computer-based approaches. According to the results, it was concluded that compounds having heteroatoms (-NCH3) in the aliphatic linker and the addition of a Br atom and a methoxy substituent at the C-2 and C-6 positions on the benzene ring, exhibit strong interactions with the AGP binding site. These compounds are identified as potential candidates for recognition by this protein. MD studies indicated that the tested analogues complexed with AGPs reach an equilibrium state after 60 ns, suggesting the stability of the complexes. This observation was further corroborated by experimental results. Therefore, exploring the interaction mechanism of pentamidine derivatives with plasma proteins holds promise for the development of bis-benzamidine-designed pharmaceutically important drugs.

Simulation Models for Prediction of Bioavailability of Medicinal Drugs-the Interface Between Experiment and Computation

The oral drug bioavailability (BA) problems have remained inevitable over the years, impairing drug efficacy and indirectly leading to eventual human morbidity and mortality. However, some conventional lab-based methods improve drug absorption leading to enhanced BA, and the recent experimental techniques are up-and-coming. Nevertheless, some have inherent drawbacks in improving the efficacy of poorly insoluble and low impermeable drugs. Drug BA and strategies to overcome these challenges were briefly highlighted. This review has significantly unravelled the different computational models for studying and predicting drug bioavailability. Several computational approaches provide mechanistic insights into the oral drug delivery system simulation of descriptors like solubility, permeability, transport protein-ligand interactions, and molecular structures. The in silico techniques have long been known still are just being applied to unravel drug bioavailability issues. Many publications have reported novel applications of the computational models towards achieving improved drug BA, including predicting gastrointestinal tract (GIT) drug absorption properties and passive intestinal membrane permeability, thus maximizing time and resources. Also, the classical molecular simulation models for free solvation energies of soluble-related processes such as solubilization, dissolutions, supersaturation, and precipitation have been used in virtual screening studies. A few of the tools are GastroPlus^TM that supports biowaiver for drugs, mainly BCS class III and predicts drug compounds' absorption and pharmacokinetic process; SimCyp® simulator for mechanistic modelling and simulation of drug formulation processes; pharmacodynamics analysis on non-linear mixed-effects modelling; and mathematical models, predicting absorption potential/maximum absorption dose. This review provides in silico-experiment annexation in the drug bioavailability enhancement, possible insights that lead to critical opinion on the applications and reliability of the various in silico models as a growing tool for drug development and discovery, thus accelerating drug development processes.

Using computers to ESKAPE the antibiotic resistance crisis

Since the discovery of penicillin, the development and use of antibiotics have promoted safe and effective control of bacterial infections. However, the number of antibiotic-resistance cases has been ever increasing over time. Thus, the drug discovery process demands fast, efficient and cost-effective alternative approaches for developing lead candidates with outstanding performance. Computational approaches are appealing techniques to develop lead candidates in an in silico fashion. In this review, we provide an overview of the implementation of current in silico state-of-the-art techniques, including machine learning (ML) and deep learning (DL), in drug discovery. We also discuss the development of quantum computing and its potential benefits for antibiotics research and current bottlenecks that limit computational drug discovery advancement.

An Overview of Antiretroviral Agents for Treating HIV Infection in Paediatric Population

Paediatric Acquired ImmunoDeficiency Syndrome (AIDS) is a life-threatening and infectious disease in which the Human Immunodeficiency Virus (HIV) is mainly transmitted through Mother-To- Child Transmission (MTCT) during pregnancy, labour and delivery, or breastfeeding. This review provides an overview of the distinct therapeutic alternatives to abolish the systemic viral replication in paediatric HIV-1 infection. Numerous classes of antiretroviral agents have emerged as therapeutic tools for downregulation of different steps in the HIV replication process. These classes encompass Non- Nucleoside Analogue Reverse Transcriptase Inhibitors (NNRTIs), Nucleoside/Nucleotide Analogue Reverse Transcriptase Inhibitors (NRTIs/NtRTIs), INtegrase Inhibitors (INIs), Protease Inhibitors (PIs), and Entry Inhibitors (EIs). Co-administration of certain antiretroviral drugs with Pharmacokinetic Enhancers (PEs) may boost the effectiveness of the primary therapeutic agent. The combination of multiple antiretroviral drug regimens (Highly Active AntiRetroviral Therapy - HAART) is currently the standard therapeutic approach for HIV infection. So far, the use of HAART offers the best opportunity for prolonged and maximal viral suppression, and preservation of the immune system upon HIV infection. Still, the frequent administration of high doses of multiple drugs, their inefficient ability to reach the viral reservoirs in adequate doses, the development of drug resistance, and the lack of patient compliance compromise the complete HIV elimination. The development of nanotechnology-based drug delivery systems may enable targeted delivery of antiretroviral agents to inaccessible viral reservoir sites at therapeutic concentrations. In addition, the application of Computer-Aided Drug Design (CADD) approaches has provided valuable tools for the development of anti-HIV drug candidates with favourable pharmacodynamics and pharmacokinetic properties.

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.

Computational modeling of human oral bioavailability: what will be next?

INTRODUCTION

The oral route is the most convenient way of administrating drugs. Therefore, accurate determination of oral bioavailability is paramount during drug discovery and development. Quantitative structure-property relationship (QSPR), rule-of-thumb (RoT) and physiologically based-pharmacokinetic (PBPK) approaches are promising alternatives to the early oral bioavailability prediction. Areas covered: The authors give insight into the factors affecting bioavailability, the fundamental theoretical framework and the practical aspects of computational methods for predicting this property. They also give their perspectives on future computational models for estimating oral bioavailability. Expert opinion: Oral bioavailability is a multi-factorial pharmacokinetic property with its accurate prediction challenging. For RoT and QSPR modeling, the reliability of datasets, the significance of molecular descriptor families and the diversity of chemometric tools used are important factors that define model predictability and interpretability. Likewise, for PBPK modeling the integrity of the pharmacokinetic data, the number of input parameters, the complexity of statistical analysis and the software packages used are relevant factors in bioavailability prediction. Although these approaches have been utilized independently, the tendency to use hybrid QSPR-PBPK approaches together with the exploration of ensemble and deep-learning systems for QSPR modeling of oral bioavailability has opened new avenues for development promising tools for oral bioavailability prediction.

The advancement of multidimensional QSAR for novel drug discovery - where are we headed?

INTRODUCTION

The Multidimensional quantitative structure-activity relationship (multidimensional-QSAR) method is one of the most popular computational methods employed to predict interesting biochemical properties of existing or hypothetical molecules. With continuous progress, the QSAR method has made remarkable success in various fields, such as medicinal chemistry, material science and predictive toxicology. Areas covered: In this review, the authors cover the basic elements of multidimensional -QSAR including model construction, validation and application. It includes and emphasizes the very recent developments of multidimensional -QSAR such as: HQSAR, G-QSAR, MIA-QSAR, multi-target QSAR. The advantages and disadvantages of each method are also discussed and typical examples of their application are detailed. Expert opinion: Although there are defects in multidimensional-QSAR modeling, it is still of enormous help to chemists, biologists and other researchers in various fields. In the authors' opinion, the latest more precise and feasible QSAR models should be further developed by integrating new descriptors, algorithms and other relevant computational techniques. Apart from being applied in traditional fields (e.g. lead optimization and predictive risk assessment), QSAR should be used more widely as a routine method in other emerging research fields including the modeling of nanoparticles(NPs), mixture toxicity and peptides.

Computational strategies to explore antimalarial thiazine alkaloid lead compounds based on an Australian marine sponge Plakortis Lita

In this work, an attempt was made to propose new leads based on the natural scaffold Thiaplakortone-A active against malaria. The 2D QSAR studies suggested that three descriptors correlate with the anti-malarial activity with an R² value of 0.814. Robustness, reliability, and predictive power of the model were tested by internal validation, external validation, Y-scrambling, and applicability domain analysis. HQSAR studies were carried out as an additional tool to find the sub-structural fingerprints. The CoMFA and CoMSIA models gave Q² values of 0.813 and 0.647, and [Formula: see text] values of 0.994 and 0.984, respectively. Using the 2D-QSAR equation, the activity values of the seven modified compounds were calculated and it was found that three molecules showed good anti-malarial activity. Molecular docking of the 42 Thiaplakortone-A derivatives with Plasmodium falciparum calcium-dependent protein kinase 1 (PfCDPK1) was carried out to find out protein-ligand interactions. Data mining of the bioassay data-set AID: 504850 using the classifier based on Random Forest of Weka suggested that all of the eight molecules selected and three out of the seven virtual molecules were anti-malarial active. Both the virtual molecules and drug molecules were docked with CYP3A4, indicating that the virtual molecules could metabolize easily. Toxicity studies using Osiris shows that three molecules showed no toxic characters.

A Fragment-Based Approach for the Computational Prediction of the Nonspecific Binding of Drugs to Hepatic Microsomes

Correction for the nonspecific binding (NSB) of drugs to liver microsomes is essential for the accurate measurement of the kinetic parameters K_m and K_i, and hence in vitro-in vivo extrapolation to predict hepatic clearance and drug-drug interaction potential. Although a number of computational approaches for the estimation of drug microsomal NSB have been published, they generally rely on compound lipophilicity and charge state at the expense of other physicochemical and chemical properties. In this work, we report the development of a fragment-based hologram quantitative structure activity relationship (HQSAR) approach for the prediction of NSB using a database of 132 compounds. The model has excellent predictivity, with a noncross-validated r² of 0.966 and cross-validated r² of 0.680, with a predictive r² of 0.748 for an external test set comprising 34 drugs. The HQSAR method reliably predicted the fraction unbound in incubations of 95% of the training and test set drugs, excluding compounds with a steroid or morphinan 4,5-epoxide nucleus. Using the same data set of compounds, performance of the HQSAR method was superior to a model based on logP/D as the sole descriptor (predictive r² for the test set compounds, 0.534). Thus, the HQSAR method provides an alternative approach to laboratory-based procedures for the prediction of the NSB of drugs to liver microsomes, irrespective of the drug charge state (acid, base, or neutral).

QSPR models for predicting generator-column-derived octanol/water and octanol/air partition coefficients of polychlorinated biphenyls

Octanol/water (K(OW)) and octanol/air (K(OA)) partition coefficients are two important physicochemical properties of organic substances. In current practice, K(OW) and K(OA) values of some polychlorinated biphenyls (PCBs) are measured using generator column method. Quantitative structure-property relationship (QSPR) models can serve as a valuable alternative method of replacing or reducing experimental steps in the determination of K(OW) and K(OA). In this paper, two different methods, i.e., multiple linear regression based on dragon descriptors and hologram quantitative structure-activity relationship, were used to predict generator-column-derived log K(OW) and log K(OA) values of PCBs. The predictive ability of the developed models was validated using a test set, and the performances of all generated models were compared with those of three previously reported models. All results indicated that the proposed models were robust and satisfactory and can thus be used as alternative models for the rapid assessment of the K(OW) and K(OA) of PCBs.

Linking existing in vitro dermal absorption data to physicochemical properties: Contribution to the design of a weight-of-evidence approach for the safety evaluation of cosmetic ingredients with low dermal bioavailability

To characterize the risk of cosmetic ingredients when threshold toxicity is assumed, often the "margin of safety" (MoS) is calculated. This uncertainty factor is based on the systemic no observable (adverse) effect level (NO(A)EL) which can be derived from in vivo repeated dose toxicity studies. As in vivo studies for the purpose of the cosmetic legislation are no longer allowed in Europe and a validated in vitro alternative is not yet available, it is no longer possible to derive a NO(A)EL value for a new cosmetic ingredient. Alternatively, cosmetic ingredients with a low dermal bioavailability might not need repeated dose data, as internal exposure will be minimal and systemic toxicity might not be an issue. This study shows the possibility of identifying compounds suspected to have a low dermal bioavailability based on their physicochemical properties (molecular weight, melting point, topological polar surface area and log P) and their in vitro dermal absorption data. Although performed on a limited number of compounds, the study suggests a strategic opportunity to support the safety assessor's reasoning to omit a MoS calculation and to focus more on local toxicity and mutagenicity/genotoxicity for ingredients for which limited systemic exposure is to be expected.

QSAR analysis of thiolactone derivatives using HQSAR, CoMFA and CoMSIA

The development of resistant malaria and lethality of the disease demands the search for new therapeutic candidates. In this line-up, thiolactone was identified as the potential lead structure and subjected to hologram quantitative structure-activity relationship (HQSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Overall, the QSAR results shows that the LOO cross-validated q(2) values of HQSAR, CoMFA and CoMSIA models are 0.791, 0.737 and 0.753, respectively. According to HQSAR, the hydrogen bond donor and acceptor were found to play an important role in governing antimalarial activity of thiolactone derivatives. The fragment contribution map of HQSAR, and contour maps of CoMFA and CoMSIA showed the presence of an electronegative group at the fifth position, and a bulky group at the third and fourth positions of the thiolactone ring, positively contributing to antimalarial activity. Furthermore, molecular docking was performed to analyze the binding mode of newly designed thiolactones with the active site residues of pf KAS I/II. The prediction of newly designed thiolactone molecules based on QSAR and docking score are in good accordance with each other. Therefore the ligand-based QSAR models and target structure-based docking model developed in this study may be successfully utilized for the design of new antimalarial agents.

Hologram quantitative structure–activity relationship and comparative molecular interaction field analysis of aminothiazole and thiazolesulfonamide as reversible LSD1 inhibitors

Background: LSD-1 is an enzyme that removes methyl groups from lysine residues of histone proteins. LSD-1 inhibition decreases cellular proliferation and therefore represents a therapeutic target for cancer treatment. MAO and LSD-1 are both flavin adenine dinucleotide-dependent MAOs, and the MAO inhibitor, tranylcypromine, is currently undergoing clinical trials for cancer treatment because it acts as an irreversible LSD-1 inhibitor. Materials & methods: The present study investigated new reversible LSD-1 inhibitors, in order to develop novel selective anticancer agents. We constructed 2 and 3D quantitative structure–activity relationship models by using a series of 54 aminothiazole and thiazolesulfonamide derivatives. Results: The models were validated internally and externally (q2 , 0.691 and 0.701; r2 , 0.894 and 0.937; r2 test , 0.785 and 0.644, for 2 and 3D models, respectively). Fragment contribution maps, as well as steric and electrostatic contour maps were generated in order to obtain chemical information related to LSD-1 inhibition. Conclusion: The thiazolesulfonamide group was fundamental to the inhibition of LSD-1 by these compounds and that bulky and aromatic substituents at the thiazole ring were important for their steric and electrostatic interactions with the active site of LSD-1.

Advances in computationally modeling human oral bioavailability

Although significant progress has been made in experimental high throughput screening (HTS) of ADME (absorption, distribution, metabolism, excretion) and pharmacokinetic properties, the ADME and Toxicity (ADME-Tox) in silico modeling is still indispensable in drug discovery as it can guide us to wisely select drug candidates prior to expensive ADME screenings and clinical trials. Compared to other ADME-Tox properties, human oral bioavailability (HOBA) is particularly important but extremely difficult to predict. In this paper, the advances in human oral bioavailability modeling will be reviewed. Moreover, our deep insight on how to construct more accurate and reliable HOBA QSAR and classification models will also discussed.

Applying machine learning techniques for ADME-Tox prediction: a review

INTRODUCTION

Pharmacokinetics involves the study of absorption, distribution, metabolism, excretion and toxicity of xenobiotics (ADME-Tox). In this sense, the ADME-Tox profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safety were considered some of the major causes of clinical failures in the development of new chemical entities. In this context, machine learning (ML) techniques have been often used in ADME-Tox studies due to the existence of compounds with known pharmacokinetic properties available for generating predictive models.

AREAS COVERED

This review examines the growth in the use of some ML techniques in ADME-Tox studies, in particular supervised and unsupervised techniques. Also, some critical points (e.g., size of the data set and type of output variable) must be considered during the generation of models that relate ADME-Tox properties and biological activity.

EXPERT OPINION

ML techniques have been successfully employed in pharmacokinetic studies, helping the complex process of designing new drug candidates from the use of reliable ML models. An application of this procedure would be the prediction of ADME-Tox properties from studies of quantitative structure-activity relationships or the discovery of new compounds from a virtual screening using filters based on results obtained from ML techniques.

Critical evaluation of human oral bioavailability for pharmaceutical drugs by using various cheminformatics approaches

PURPOSE

Oral bioavailability (%F) is a key factor that determines the fate of a new drug in clinical trials. Traditionally, %F is measured using costly and time-consuming experimental tests. Developing computational models to evaluate the %F of new drugs before they are synthesized would be beneficial in the drug discovery process.

METHODS

We employed Combinatorial Quantitative Structure-Activity Relationship approach to develop several computational %F models. We compiled a %F dataset of 995 drugs from public sources. After generating chemical descriptors for each compound, we used random forest, support vector machine, k nearest neighbor, and CASE Ultra to develop the relevant QSAR models. The resulting models were validated using five-fold cross-validation.

RESULTS

The external predictivity of %F values was poor (R(2) = 0.28, n = 995, MAE = 24), but was improved (R(2) = 0.40, n = 362, MAE = 21) by filtering unreliable predictions that had a high probability of interacting with MDR1 and MRP2 transporters. Furthermore, classifying the compounds according to the %F values (%F < 50% as "low", %F ≥ 50% as 'high") and developing category QSAR models resulted in an external accuracy of 76%.

CONCLUSIONS

In this study, we developed predictive %F QSAR models that could be used to evaluate new drug compounds, and integrating drug-transporter interactions data greatly benefits the resulting models.

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.

Systems biological approach of molecular descriptors connectivity: optimal descriptors for oral bioavailability prediction

BACKGROUND

Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties.

RESULTS

The models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/-bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction.

CONCLUSION

The logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability.

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.

A prediction model for oral bioavailability of drugs using physicochemical properties by support vector machine

OBJECTIVE

A computational model for predicting oral bioavailability is very important both in the early stage of drug discovery to select the promising compounds for further optimizations and in later stage to identify candidates for clinical trials. In present study, we propose a support vector machine (SVM)-based kernel learning approach carried out at a set of 511 chemically diverse compounds with known oral bioavailability values.

MATERIAL AND METHODS

For each drug, 12 descriptors were calculated. The selection of optimal hyper-plane parameters was performed with 384 training set data and the prediction efficiency of proposed classifier was tested on 127 test set data.

RESULTS

The overall prediction efficiency for the test set came out to be 96.85%. Youden's index and Matthew correlation index were found to be 0.929 and 0.909, respectively. The area under receiver operating curve (ROC) was found to be 0.943 with standard error 0.0253.

CONCLUSION

The prediction model suggests that while considering chemoinformatics approaches into account, SVM-based prediction of oral bioavailability can be a significantly important tool for drug development and discovery at a preliminary level.

ADME evaluation in drug discovery. 9. Prediction of oral bioavailability in humans based on molecular properties and structural fingerprints

Oral bioavailability is an essential parameter in drug screening cascades and a good indicator of the capability of the delivery of a given compound to the systemic circulation by oral administration. In the present work, we report a database of oral bioavailability of 1014 molecules determined in humans. A systematic examination of the relationships between various physicochemical properties and oral bioavailability were carried out to investigate the influence of these properties on oral bioavailability. A number of property-based rules for bioavailability classification were generated and evaluated. We found that no rule was an effective predictor for oral bioavailability because these simple rules cannot characterize the influence of important metabolic processes on bioavailability. Finally, the genetic function approximation (GFA) technique was employed to construct the multiple linear regression models for oral bioavailability using structural fingerprints as the basic parameters, together with several important molecular properties. The best model is able to predict human oral bioavailability with an r of 0.79, a q of 0.72, and a RMSE (root-mean-square error) of 22.30% of the compounds from the training set. The analysis of the descriptors chosen by GFA shows that the important structural fingerprints are primarily related to important intestinal absorption and well-known metabolic processes. The predictive power of the models was further evaluated using a separate test set of 80 compounds, and the consensus model can predict the oral bioavailability with r(test) = 0.71 and RMSE = 23.55% for the tested compounds. Since the necessary molecular properties and structural fingerprints can be calculated easily and quickly, the models we proposed here may help speed up the process of finding or designing compounds with improved oral bioavailability.

Toxicokinetic Considerations in Predicting Toxicity

The ability of a compound to elicit a toxic effect within an organism is dependent upon three factors (i) the external exposure of the organism to the toxicant in the environment or via the food chain (ii) the internal uptake of the compound into the organism and its transport to the site of action in sufficient concentration and (iii) the inherent toxicity of the compound. The in silico prediction of toxicity and the role of external exposure have been dealt with in other chapters of this book. This chapter focuses on the importance of ‘internal exposure’ i.e. the absorption, distribution, metabolism and elimination (ADME) properties of compounds which determine their toxicokinetic profile. An introduction to key concepts in toxicokinetics will be provided, along with examples of modelling approaches and software available to predict these properties. A brief introduction will also be given into the theory of physiologically-based toxicokinetic modelling.

De novo design: balancing novelty and confined chemical space

IMPORTANCE OF THE FIELD

De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner.

AREAS COVERED IN THIS REVIEW

This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation.

WHAT THE READER WILL GAIN

The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future.

TAKE HOME MESSAGE

De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.

Identification of Metabotropic Glutamate Receptor Subtype 5 Potentiators Using Virtual High-Throughput Screening

Selective potentiators of glutamate response at metabotropic glutamate receptor subtype 5 (mGluR5) have exciting potential for the development of novel treatment strategies for schizophrenia. A total of 1,382 compounds with positive allosteric modulation (PAM) of the mGluR5 glutamate response were identified through high-throughput screening (HTS) of a diverse library of 144,475 substances utilizing a functional assay measuring receptor-induced intracellular release of calcium. Primary hits were tested for concentration-dependent activity, and potency data (EC₅₀ values) were used for training artificial neural network (ANN) quantitative structure−activity relationship (QSAR) models that predict biological potency from the chemical structure. While all models were trained to predict EC₅₀, the quality of the models was assessed by using both continuous measures and binary classification. Numerical descriptors of chemical structure were used as input for the machine learning procedure and optimized in an iterative protocol. The ANN models achieved theoretical enrichment ratios of up to 38 for an independent data set not used in training the model. A database of ∼450,000 commercially available drug-like compounds was targeted in a virtual screen. A set of 824 compounds was obtained for testing based on the highest predicted potency values. Biological testing found 28.2% (232/824) of these compounds with various activities at mGluR5 including 177 pure potentiators and 55 partial agonists. These results represent an enrichment factor of 23 for pure potentiation of the mGluR5 glutamate response and 30 for overall mGluR5 modulation activity when compared with those of the original mGluR5 experimental screening data (0.94% hit rate). The active compounds identified contained 72% close derivatives of previously identified PAMs as well as 28% nontrivial derivatives of known active compounds.

Troubleshooting computational methods in drug discovery

Computational approaches for drug discovery such as ligand-based and structure-based methods, are increasingly seen as an efficient approach for lead discovery as well as providing insights on absorption, distribution, metabolism, excretion and toxicity (ADME/Tox). What is perhaps less well known and widely described are the limitations of the different technologies. We have therefore presented a troubleshooting approach to QSAR, homology modeling, docking as well as hybrid methods. If such computational or cheminformatics methods are to become more widely used by non-experts it is critical that such limitations are brought to the user's attention and addressed during their workflows. This could improve the quality of the models and results that are obtained.

Transport and dynamics of toxic pollutants in the natural environment and their effect on human health: research gaps and challenge

The source-pathway-receptor (SPR) approach to human exposure and risk assessment contains considerable uncertainty when using the refined modelling approaches to pollutant transport and dispersal, not least in how compounds of concern might be prioritized, proxy or indicator substances identified and the basic environmental and toxicological data collected. The impact of external environmental variables, urban systems and lifestyle is still poorly understood. This determines exposure of individuals and there are a number of methods being developed to provide more reliable spatial assessments. Within the human body, the dynamics of pollutants and effects on target organs from diffuse, transient sources of exposure sets ambitious challenges for traditional risk assessment approaches. Considerable potential exists in the application of, e.g. physiologically based pharmacokinetic (PBPK) models. The reduction in uncertainties associated with the effects of contaminants on humans, transport and dynamics influencing exposure, implications of adult versus child exposure and lifestyle and the development of realistic toxicological and exposure data are all highlighted as urgent research needs. The potential to integrate environmental with toxicological models provides the next phase of research opportunity and should be used to drive empirical and model assessments.

Structure-based approach for the study of estrogen receptor binding affinity and subtype selectivity

Estrogens exert important physiological effects through the modulation of two human estrogen receptor (hER) subtypes, alpha (hERalpha) and beta (hERbeta). Because the levels and relative proportion of hERalpha and hERbeta differ significantly in different target cells, selective hER ligands could target specific tissues or pathways regulated by one receptor subtype without affecting the other. To understand the structural and chemical basis by which small molecule modulators are able to discriminate between the two subtypes, we have applied three-dimensional target-based approaches employing a series of potent hER-ligands. Comparative molecular field analysis (CoMFA) studies were applied to a data set of 81 hER modulators, for which binding affinity values were collected for both hERalpha and hERbeta. Significant statistical coefficients were obtained (hERalpha, q(2) = 0.76; hERbeta, q(2) = 0.70), indicating the internal consistency of the models. The generated models were validated using external test sets, and the predicted values were in good agreement with the experimental results. Five hER crystal structures were used in GRID/PCA investigations to generate molecular interaction fields (MIF) maps. hERalpha and hERbeta were separated using one factor. The resulting 3D information was integrated with the aim of revealing the most relevant structural features involved in hER subtype selectivity. The final QSAR and GRID/PCA models and the information gathered from 3D contour maps should be useful for the design of novel hER modulators with improved selectivity.