In Silico Absorption, Distribution, Metabolism, Excretion, and Pharmacokinetics (ADME-PK): Utility and Best Practices. An Industry Perspective from the International Consortium for Innovation through Quality in Pharmaceutical Development

Rational design of potent anti-COVID-19 main protease drugs: An extensive multi-spectrum in silico approach

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a novel coronavirus and the etiological agent of global pandemic coronavirus disease (COVID-19) requires quick development of potential therapeutic strategies. Computer aided drug design approaches are highly efficient in identifying promising drug candidates among an available pool of biological active antivirals with safe pharmacokinetics. The main protease (M^Pro) enzyme of SARS-CoV-2 is considered key in virus production and its crystal structures are available at excellent resolution. This marks the enzyme as a good starting receptor to conduct an extensive structure-based virtual screening (SBVS) of ASINEX antiviral library for the purpose of uncovering valuable hits against SARS-CoV-2 M^Pro. A compound hit (BBB_26580140) was stand out in the screening process, as opposed to the control, as a potential inhibitor of SARS-CoV-2 M^Pro based on a combined approach of SBVS, drug likeness and lead likeness annotations, pharmacokinetics, molecular dynamics (MD) simulations, and end point MM-PBSA binding free energy methods. The lead was further used in ligand-based similarity search (LBSS) that found 33 similar compounds from the ChEMBL database. A set of three compounds (SCHEMBL12616233, SCHEMBL18616095, and SCHEMBL20148701), based on their binding affinity for M^Pro, was selected and analyzed using extensive MD simulation, hydrogen bond profiling, MM-PBSA, and WaterSwap binding free energy techniques. The compounds conformation with M^Pro show good stability after initial within active cavity moves, a rich intermolecular network of chemical interactions, and reliable relative and absolute binding free energies. Findings of the study suggested the use of BBB_26580140 lead and its similar analogs to be explored in vivo which might pave the path for rational drug discovery against SARS-CoV-2 M^Pro.

Recent advances in computational metabolite structure predictions and altered metabolic pathways assessment to inform drug development processes

Many drug candidates fail during preclinical and clinical trials due to variable or unexpected metabolism which may lead to variability in drug efficacy or adverse drug reactions. The drug metabolism field aims to address this important issue from many angles which range from the study of drug-drug interactions, pharmacogenomics, computational metabolic modeling, and others. This manuscript aims to provide brief but comprehensive manuscript summaries highlighting the conclusions and scientific importance of seven exceptional manuscripts published in recent years within the field of drug metabolism. Two main topics within the field are reviewed: novel computational metabolic modeling approaches which provide complex outputs beyond site of metabolism predictions, and experimental approaches designed to discern the impacts of interindividual variability and species differences on drug metabolism. The computational approaches discussed provide novel outputs in metabolite structure and formation likelihood and/or extend beyond the saturated field of drug phase I metabolism, while the experimental metabolic pathways assessments aim to highlight the impacts of genetic polymorphisms and clinical animal model metabolic differences on human metabolism and subsequent health outcomes.

Discovery of 5-(or 6)-benzoxazoles and oxazolo[4,5-b]pyridines as novel candidate antitumor agents targeting hTopo IIα

Discovery of novel anticancer drugs which have low toxicity and high activity is very significant area in anticancer drug research and development. One of the important targets for cancer treatment research is topoisomerase enzymes. In order to make a contribution to this field, we have designed and synthesized some 5(or 6)-nitro-2-(substitutedphenyl)benzoxazole (1a-1r) and 2-(substitutedphenyl)oxazolo[4,5-b]pyridine (2a-2i) derivatives as novel candidate antitumor agents targeting human DNA topoisomerase enzymes (hTopo I and hTopo IIα). Biological activity results were found very promising for the future due to two compounds, 5-nitro-2-(4-butylphenyl)benzoxazole (1i) and 2-(4-butylphenyl)oxazolo[4,5-b]pyridine (2i), that inhibited hTopo IIα with 2 µM IC₅₀ value. These two compounds were also found to be more active than reference drug etoposide. However, 1i and 2i did not show any satisfactory cyctotoxic activity on the HeLa, WiDR, A549, and MCF7 cancer cell lines. Moreover, molecular docking and molecular dynamic simulations studies for the most active compounds were applied in order to understand the mechanism of inhibition activity of hTopo IIα. In addition, in silico ADME/Tox studies were performed to predict drug-likeness and pharmacokinetic properties of all the tested compounds.

Clinical pharmacokinetic study of latrepirdine via in silico sublingual administration

In recent decades, numerous in silico methodologies have been developed focused on the study of pharmacodynamic, pharmacokinetics and toxicological properties of drugs. The study of the pharmacokinetic behavior of new chemical entities is an essential part of the successful development of a new drug and Gastroplus™ is a simulation software used to predict the pharmacokinetic behavior of chemical entities. Latrepirdine is a drug that has been studied for Alzheimer's disease and Huntington's disease and later abandoned by the pharmaceutical industry already in the clinical trials because it has not demonstrated therapeutic efficacy. During this project, through Gastroplus™ simulations, it was possible to achieve predicted values of C_max coincident with those found in clinical trials, showing its utility in the prediction of pharmacokinetic parameters. Besides, sublingual delivery has the potential to offer improved bioavailability by circumventing first-pass metabolism. This study used GastroPlus™ to simulate sublingual administration of latrepirdine and the results showed improvements in bioavailability and plasma concentrations achieved though this route of administration.

Benzosuberene-sulfone analogues synthesis from Cedrus deodara oil and their therapeutic evaluation by computational analysis to treat type 2 diabetes

Benzosuberene-sulfone (BSS) analogues have been semi-synthesized following green approaches from himachalenes, which has been extracted from essential oil of Cedrus deodara. In this process, benzosuberene in presence of different aryl or alkyl sodium sulfinates, I₂ and potassium persulfate (K₂S₂O₈) in acetonitrile-water solvent conditions gave BSS-analogues at room temperature. Under this reaction, a facile endocyclic β-H elimination has been noticed for BSS-analogues synthesis instead of vinyl sulfones and the reason may be due to its specific structure and electronic environment. The BSS-compounds were obtained with moderate to excellent yields under mild conditions. All the compounds were computationally subjected to drug likeliness and toxicity prediction studies. Further, the synthesized molecules were evaluated under in-silico studies for their binding affinity towards the native Peroxisome Proliferator-Activated Receptor Gamma (PPARG), and two PPARG mutants (R357A and V290M). Both the mutant forms of PPARG are deficient in eliciting a response to treatment with full and partial agonists. Our computational studies suggested that the molecule 3q performed better than the standard drug (Rosiglitazone) in all three protein structures. This implies that our suggested molecule could act as a more potent antagonist to native PPARG and could also be developed to treat type-2 diabetes patients with R357A and V290M mutations, which didn't elicit any response to currently available drugs in the market.

Discovery of novel inhibitors against main protease (Mpro) of SARS-CoV-2 via virtual screening and biochemical evaluation

SARS-CoV-2 is the pathogen that caused the global COVID-19 outbreak in 2020. Promising progress has been made in developing vaccines and antiviral drugs. Antivirals medicines are necessary complements of vaccines for post-infection treatment. The main protease (Mpro) is an extremely important protease in the reproduction process of coronaviruses which cleaves pp1ab over more than 11 cleavage sites. In this work, two active main protease inhibitors were found via docking-based virtual screening and bioassay. The IC₅₀ of compound VS10 was 0.20 μM, and the IC₅₀ of compound VS12 was 1.89 μM. The finding in this work can be helpful to understand the interactions of main protease and inhibitors. The active candidates could be potential lead compounds for future drug design.

Drug screening using shape-based virtual screening and in vitro experimental models of cutaneous Leishmaniasis

Cutaneous leishmaniasis (CL) is one of the most disregarded tropical neglected disease with the occurrence of self-limiting ulcers and triggering mucosal damage and stigmatizing scars, leading to huge public health problems and social negative impacts. Pentavalent antimonials are the first-line drug for CL treatment for over 70 years and present several drawbacks in terms of safety and efficacy. Thus, there is an urgent need to search for non-invasive, non-toxic and potent drug candidates for CL. In this sense, we have implemented a shape-based virtual screening approach and identified a set of 32 hit compounds. In vitro phenotypic screenings were conducted using these hit compounds to check their potential leishmanicidal effect towards Leishmania amazonensis (L. amazonensis). Two (Cp1 and Cp2) out of the 32 compounds revealed promising antiparasitic activities, exhibiting considerable potency against intracellular amastigotes present in peritoneal macrophages (IC50 values of 9.35 and 7.25 μm, respectively). Also, a sterile cidality profile was reached at 20 μm after 48 h of incubation, besides a reasonable selectivity (≈8), quite similarly to pentamidine, a diamidine still in use clinically for leishmaniasis. Cp1 with an oxazolo[4,5-b]pyridine scaffold and Cp2 with benzimidazole scaffold could be developed by lead optimization studies to enhance their leishmanicidal potency.

Evaluation of acridinedione analogs as potential SARS-CoV-2 main protease inhibitors and their comparison with repurposed anti-viral drugs

Background

The main protease (Mpro) of SARS-CoV-2 is involved in the processing of vital polypeptides required for viral genome replication and transcription and is one of the best-characterized targets to inhibit the progression of SARS-CoV-2 in infected individuals.

Methods

We screened a set of novel classes of acridinediones molecules to efficiently bind and inhibit the activity of the SARS-CoV-2 by targeting the Mpro. The repurposed FDA-approved antivirals were taken as standard molecules for this study. Long term (1.1 μs) MD simulations were performed to analyze the conformational space of the binding pocket of Mpro bound to the selected molecules.

Results

The molecules DSPD-2 and DSPD-6 showed more favorable MM-PBSA interaction energies and were seated more deeply inside the binding pocket of Mpro than the topmost antiviral drug (Saquinavir). Moreover, DSPD-5 also exhibited comparable binding energy to Saquinavir. The analysis of per residue contribution energy and SASA studies indicated that the molecules showed efficient binding by targeting the S1 subsite of the Mpro binding pocket.

Conclusion

The DSPD-2, DSPD-6, and DSPD-5 could be developed as potential inhibitors of SARS-CoV-2. Moreover, we suggest that targeting molecules to bind effectively to the S1 subsite could potentially increase the binding of molecules to the SARS-CoV-2 Mpro.

•

A robust computational strategy applied to identify the potential lead for COVID-19.

•

Repurposed FDA approved antiviral drugs were compared with a set of acridinedione analogs against Mpro of SARS-CoV-2.

•

The acridinedione analogs have acceptable ADMET values and low toxicity profile.

•

In-house synthesized acridinedione analogs showed good amount of interaction with Mpro of SARS-CoV-2.

Discovery and in silico evaluation of aminoarylbenzosuberene molecules as novel checkpoint kinase 1 inhibitor determinants

Checkpoint kinase 1 (CHK1) is an essential kinase with a critical function in cell cycle arrest. Several potent inhibitors targeting CHK1 have been published, but most of them have failed in clinical trials. Acknowledging the emerging consequence of CHK1 inhibitors in medication of cancer, there is a demand for widening the chemical range of CHK1 inhibitors. In this research, we considered a set of in-house plant based semi-synthetic aminoarylbenzosuberene molecules as potential CHK1 inhibitors. Based on a combined computational research that consolidates molecular docking and binding free energy computations we recognized the crucial determinants for their receptor binding. The drug likeness of these molecules were also scrutinized based on their toxicity and bioavailibilty profile. The computational strategy indicates that the Bch10 could be regarded as a potential CHK1 inhibitor in comparison with top five co-crystallize molecules. Bch10 signifies a promising outlet for the development of potent inhibitors for CHK1.

Development of multitarget inhibitors for the treatment of pain: Design, synthesis, biological evaluation and molecular modeling studies

Multitarget-directed ligands are a promising class of drugs for discovering innovative new therapies for difficult to treat diseases. In this study, we designed dual inhibitors targeting the human fatty acid amide hydrolase (FAAH) enzyme and human soluble epoxide hydrolase (sEH) enzyme. Targeting both of these enzymes concurrently with single target inhibitors synergistically reduces inflammatory and neuropathic pain; thus, dual FAAH/sEH inhibitors are likely to be powerful analgesics. Here, we identified the piperidinyl-sulfonamide moiety as a common pharmacophore and optimized several inhibitors to have excellent inhibition profiles on both targeted enzymes simultaneously. In addition, several inhibitors show good predicted pharmacokinetic properties. These results suggest that this series of inhibitors has the potential to be further developed as new lead candidates and therapeutics in pain management.

Bayer's in silico ADMET platform: a journey of machine learning over the past two decades

Over the past two decades, an in silico absorption, distribution, metabolism, and excretion (ADMET) platform has been created at Bayer Pharma with the goal to generate models for a variety of pharmacokinetic and physicochemical endpoints in early drug discovery. These tools are accessible to all scientists within the company and can be a useful in assisting with the selection and design of novel leads, as well as the process of lead optimization. Here. we discuss the development of machine-learning (ML) approaches with special emphasis on data, descriptors, and algorithms. We show that high company internal data quality and tailored descriptors, as well as a thorough understanding of the experimental endpoints, are essential to the utility of our models. We discuss the recent impact of deep neural networks and show selected application examples.

Design, synthesis, and biological evaluation of new 6,N 2-diaryl-1,3,5-triazine-2,4-diamines as anticancer agents selectively targeting triple negative breast cancer cells

New 6,N²-diaryl-1,3,5-triazine-2,4-diamines were designed using the 3D-QSAR model developed earlier. These compounds were prepared and their antiproliferative activity was evaluated against three breast cancer cell lines (MDA-MB231, SKBR-3 and MCF-7) and non-cancerous MCF-10A epithelial breast cells. The synthesized compounds demonstrated selective antiproliferative activity against triple negative MDA-MB231 breast cancer cells. The most active compound in the series inhibited MDA-MB231 breast cancer cell growth with a GI₅₀ value of 1 nM. None of the tested compounds significantly affected the growth of the normal breast cells. The time-dependent cytotoxic effect, observed when cytotoxicity was assessed at different time intervals after the treatment, and morphological features, observed in the fluorescence microscopy and live cell imaging experiments, suggested apoptosis as the main pathway for the antiproliferative activity of these compounds against MDA-MB231 cells.

New highly potent and selective 6,N²-diaryl-1,3,5-triazine-2,4-diamines were designed and prepared using the 3D-QSAR model developed earlier.

Molecular property prediction: recent trends in the era of artificial intelligence

Artificial intelligence (AI) has become a powerful tool in many fields, including drug discovery. Among various AI applications, molecular property prediction can have more significant immediate impact to the drug discovery process since most algorithms and methods use predicted properties to evaluate, select, and generate molecules. Herein, we provide a brief review of the state-of-art molecular property prediction methodologies and discuss examples reported recently. We highlight key techniques that have been applied to molecular property prediction such as learned representation, multi-task learning, transfer learning, and federated learning. We also point out some critical but less discussed issues such as data set quality, benchmark, model performance evaluation, and prediction confidence quantification.

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.

Advances in the study of drug metabolism - symposium report of the 12th Meeting of the International Society for the Study of Xenobiotics (ISSX)

The 12th International Society for the Study of Xenobiotics (ISSX) meeting, held in Portland, OR, USA from July 28 to 31, 2019, was attended by diverse members of the pharmaceutical sciences community. The ISSX New Investigators Group provides learning and professional growth opportunities for student and early career members of ISSX. To share meeting content with those who were unable to attend, the ISSX New Investigators herein elected to highlight the "Advances in the Study of Drug Metabolism" symposium, as it engaged attendees with diverse backgrounds. This session covered a wide range of current topics in drug metabolism research including predicting sites and routes of metabolism, metabolite identification, ligand docking, and medicinal and natural products chemistry, and highlighted approaches complemented by computational modeling. In silico tools have been increasingly applied in both academic and industrial settings, alongside traditional and evolving in vitro techniques, to strengthen and streamline pharmaceutical research. Approaches such as quantum mechanics simulations facilitate understanding of reaction energetics toward prediction of routes and sites of drug metabolism. Furthermore, in tandem with crystallographic and orthogonal wet lab techniques for structural validation of drug metabolizing enzymes, in silico models can aid understanding of substrate recognition by particular enzymes, identify metabolic soft spots and predict toxic metabolites for improved molecular design. Of note, integration of chemical synthesis and biosynthesis using natural products remains an important approach for identifying new chemical scaffolds in drug discovery. These subjects, compiled by the symposium organizers, presenters, and the ISSX New Investigators Group, are discussed in this review.

Using Predicted Bioactivity Profiles to Improve Predictive Modeling

Predictive modeling is a cornerstone in early drug development. Using information for multiple domains or across prediction tasks has the potential to improve the performance of predictive modeling. However, aggregating data often leads to incomplete data matrices that might be limiting for modeling. In line with previous studies, we show that by generating predicted bioactivity profiles, and using these as additional features, prediction accuracy of biological endpoints can be improved. Using conformal prediction, a type of confidence predictor, we present a robust framework for the calculation of these profiles and the evaluation of their impact. We report on the outcomes from several approaches to generate the predicted profiles on 16 datasets in cytotoxicity and bioactivity and show that efficiency is improved the most when including the p-values from conformal prediction as bioactivity profiles.

Fundamental aspects of DMPK optimization of targeted protein degraders

Targeted protein degraders are an emerging modality. Their properties fall outside the traditional small-molecule property space and are in the 'beyond rule of 5' space. Consequently, drug discovery programs focused on developing orally bioavailable degraders are expected to face complex drug metabolism and pharmacokinetics (DMPK) challenges compared with traditional small molecules. Nevertheless, little information is available on the DMPK optimization of oral degraders. Therefore, in this review, we discuss our experience of these DMPK optimization challenges and present methodologies and strategies to overcome the hurdles dealing with this new small-molecule modality, specifically in developing oral degraders to treat cancer.

Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

Structure-Based Design of Antivirals against Envelope Glycoprotein of Dengue Virus

Dengue virus (DENV) presents a significant threat to global public health with more than 500,000 hospitalizations and 25,000 deaths annually. Currently, there is no clinically approved antiviral drug to treat DENV infection. The envelope (E) glycoprotein of DENV is a promising target for drug discovery as the E protein is important for viral attachment and fusion. Understanding the structure and function of DENV E protein has led to the exploration of structure-based drug discovery of antiviral compounds and peptides against DENV infections. This review summarizes the structural information of the DENV E protein with regards to DENV attachment and fusion. The information enables the development of antiviral agents through structure-based approaches. In addition, this review compares the potency of antivirals targeting the E protein with the antivirals targeting DENV multifunctional enzymes, repurposed drugs and clinically approved antiviral drugs. None of the current DENV antiviral candidates possess potency similar to the approved antiviral drugs which indicates that more efforts and resources must be invested before an effective DENV drug materializes.

Entering the era of computationally driven drug development

Historically, failure rates in drug development are high; increased sophistication and investment throughout the process has shifted the reasons for attrition, but the overall success rates have remained stubbornly and consistently low. Only 8% of new entities entering clinical testing gain regulatory approval, indicating that significant obstacles still exist for efficient therapeutic development. The continued high failure rate can be partially attributed to the inability to link drug exposure with the magnitude of observed safety and efficacy-related pharmacodynamic (PD) responses; frequently, this is a result of nonclinical models exhibiting poor prediction of human outcomes across a wide range of disease conditions, resulting in faulty evaluation of drug toxicology and efficacy. However, the increasing quality and standardization of experimental methods in preclinical stages of testing has created valuable data sets within companies that can be leveraged to further improve the efficiency and accuracy of preclinical prediction for both pharmacokinetics (PK) and PD. Models of Quantitative structure-activity relationships (QSAR), physiologically based pharmacokinetics (PBPK), and PK/PD relationships have also improved efficiency. Founded on a core understanding of biochemistry and physiological interactions of xenobiotics, these in silico methods have the potential to increase the probability of compound success in clinical trials. Integration of traditional computational methods with machine-learning approaches and existing internal pharma databases stands to make a fundamental impact on the speed and accuracy of predictions during the process of drug development and approval.

Using Physicochemical Measurements to Influence Better Compound Design

During the past decade, the physicochemical quality of molecules under investigation at all stages of the drug discovery process has come under particular scrutiny. The issues associated with excessive lipophilicity and poor solubility in particular are many and varied, ranging from poor outcomes in screening campaigns to promiscuity, limited and/or poorly predictable pharmacokinetic exposure, and, ultimately, greater chances of clinical failure. In this review, contemporary methods to secure key measurements are described along with their relevance to understanding the behavior of molecules in environments pertinent to pharmacological activity. Together, the various measurements contribute to predictive models of both the physicochemical properties themselves and the outcomes they influence.

High-throughput hydrogen bond strength calculation and its applications in optimizing drug ADME properties

AIM

Modifying the molecule's intrinsic hydrogen bond strength (HBS) is a useful approach in optimizing its permeability and P-glycoprotein (P-gp) efflux. Quantum mechanics (QM) based computation has been utilized to estimate the molecular intrinsic HBS. Despite its usefulness, the computation is time consuming for a large set of molecules.

METHODOLOGY/RESULTS

We introduced a fragment-based high-throughput HBS calculation method and validated it with internal and external datasets. Examples have been presented where the P-gp efflux and permeability can be optimized by modulating calculated HBS.

CONCLUSION

The results will enable medicinal chemists to calculate HBS in a high-throughput manner while optimizing permeability and P-gp efflux. This will further improve the efficiency of balancing multiple properties during drug discovery process.

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.

Walking a tightrope: drug discovery in visceral leishmaniasis

The current commitment of the pharma industry, nongovernmental organizations and academia to find better treatments against neglected tropical diseases should end decades of challenge caused by these global scourges. The initial result of these efforts has been the introduction of enhanced combinations of drugs, currently in clinical use, or formulations thereof. Phenotypic screening based on intracellular parasite infections has been revealed as the first key tool of antileishmanial drug discovery, because most first-in-class drugs entering Phase I trials were discovered this way. The professional commitment among stakeholders has enabled the availability of a plethora of new chemical entities that fit the target product profile for these diseases. However, the rate of hit discovery in leishmaniasis is far behind that for other neglected diseases. This review defends the need to develop new screening methods that consider the part played not only by intracellular parasites but also by the host's immune system to generate disease-relevant assays and improve clinical outcomes.

Application of New Cellular and Microphysiological Systems to Drug Metabolism Optimization and Their Positioning Respective to In Silico Tools

New cellular model systems for drug metabolism applications, such as advanced 2D liver co-cultures, spheroids, and microphysiological systems (MPSs), offer exciting opportunities to reproduce human biology more closely in vitro with the aim of improving predictions of pharmacokinetics, drug-drug interactions, and efficacy. These advanced cellular systems have quickly become established for human intrinsic clearance determination and have been validated for several other absorption, distribution, metabolism, and excretion (ADME) applications. Adoption will be driven through the demonstration of clear added value, for instance, by more accurate and precise clearance predictions and by more reliable extrapolation of drug interaction potential leading to faster progression to pivotal proof-of-concept studies. New experimental systems are attractive when they can (1) increase experimental capacity, removing optimization bottlenecks; (2) improve measurement quality of ADME properties that impact pharmacokinetics; and (3) enable measurements to be made that were not previously possible, reducing risk in ADME prediction and candidate selection. As new systems become established, they will find their place in the repository of tools used at different stages of the research and development process, depending on the balance of value, throughput, and cost. In this article, we give a perspective on the integration of these new methodologies into ADME optimization during drug discovery, and the likely applications and impacts on drug development.

Exploring Tunable Hyperparameters for Deep Neural Networks with Industrial ADME Data Sets

Deep learning has drawn significant attention in different areas including drug discovery. It has been proposed that it could outperform other machine learning algorithms, especially with big data sets. In the field of pharmaceutical industry, machine learning models are built to understand quantitative structure-activity relationships (QSARs) and predict molecular activities, including absorption, distribution, metabolism, and excretion (ADME) properties, using only molecular structures. Previous reports have demonstrated the advantages of using deep neural networks (DNNs) for QSAR modeling. One of the challenges while building DNN models is identifying the hyperparameters that lead to better generalization of the models. In this study, we investigated several tunable hyperparameters of deep neural network models on 24 industrial ADME data sets. We analyzed the sensitivity and influence of five different hyperparameters including the learning rate, weight decay for L2 regularization, dropout rate, activation function, and the use of batch normalization. This paper focuses on strategies and practices for DNN model building. Further, the optimized model for each data set was built and compared with the benchmark models used in production. Based on our benchmarking results, we propose several practices for building DNN QSAR models.

Pronounced activity of aromatic selenocyanates against multidrug resistant ESKAPE bacteria

Selenocyanates demonstrate pronounced activity against bacteria of the ESKAPE family, yeast and nematodes with limited cytotoxicity against human cells.

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.