Reducing safety-related drug attrition: the use of in vitro pharmacological profiling

A property-response perspective on modern toxicity assessment and drug toxicity index (DTI)

Toxicity related failures in drug discovery and clinical development have motivated scientists and regulators to develop a wide range of in-vitro, in-silico tools coupled with data science methods. Older drug discovery rules are being constantly modified to churn out any hidden predictive value. Nonetheless, the dose-response concepts remain central to all these methods. Over the last 2 decades medicinal chemists, and pharmacologists have observed that different physicochemical, and pharmacological properties capture trends in toxic responses. We propose that these observations should be viewed in a comprehensive property-response framework where dose is only a factor that modifies the inherent toxicity potential. We then introduce the recently proposed "Drug Toxicity Index (DTI)" and briefly summarize its applications. A webserver is available to calculate DTI values (https://all-tool-kit.github.io/Web-Tool.html).

Identification of First-in-Class Inhibitors of Kallikrein-Related Peptidase 6 That Promote Oligodendrocyte Differentiation

Multiple sclerosis (MS) is an autoimmune demyelinating disease of the central nervous system (CNS) that causes severe motor, sensory, and cognitive impairments. Kallikrein-related peptidase (KLK)6 is the most abundant serine protease secreted in the CNS, mainly by oligodendrocytes, the myelin-producing cells of the CNS, and KLK6 is assumed to be a robust biomarker of MS, since it is highly increased in the cerebrospinal fluid (CSF) of MS patients. Here, we report the design and biological evaluation of KLK6's low-molecular-weight inhibitors, para-aminobenzyl derivatives. Interestingly, selected hit compounds were selective of the KLK6 proteolytic network encompassing KLK1 and plasmin that also participate in the development of MS physiopathology. Moreover, hits were found noncytotoxic on primary cultures of murine neurons and oligodendrocyte precursor cells (OPCs). Among them, two compounds (32 and 42) were shown to promote the differentiation of OPCs into mature oligodendrocytes in vitro constituting thus emerging leads for the development of regenerative therapies.

Cold-Start Problems in Data-Driven Prediction of Drug-Drug Interaction Effects

Combining drugs, a phenomenon often referred to as polypharmacy, can induce additional adverse effects. The identification of adverse combinations is a key task in pharmacovigilance. In this context, in silico approaches based on machine learning are promising as they can learn from a limited number of combinations to predict for all. In this work, we identify various subtasks in predicting effects caused by drug–drug interaction. Predicting drug–drug interaction effects for drugs that already exist is very different from predicting outcomes for newly developed drugs, commonly called a cold-start problem. We propose suitable validation schemes for the different subtasks that emerge. These validation schemes are critical to correctly assess the performance. We develop a new model that obtains AUC-ROC =0.843 for the hardest cold-start task up to AUC-ROC =0.957 for the easiest one on the benchmark dataset of Zitnik et al. Finally, we illustrate how our predictions can be used to improve post-market surveillance systems or detect drug–drug interaction effects earlier during drug development.

Discovery of a novel RORγ antagonist with skin-restricted exposure for topical treatment of mild to moderate psoriasis

Clinical success of IL-17/IL-23 pathway biologics for the treatment of moderate to severe psoriasis suggests that targeting RORγt, a master regulator for the proliferation and function of Th17 cells, could be an effective alternative. However, oral RORγ antagonists (VTP43742, TAK828) with high systemic exposure showed toxicity in phase I/II clinical trials and terminated development. To alleviate the potential safety concerns, identifying compounds with skin-restricted exposure amenable for topical use is of great interest. Systematic structure activity relationship study and multi-parameter optimization led to the discovery of a novel RORγ antagonist (SHR168442) with desired properties for a topical drug. It suppressed the transcription of IL-17 gene, leading to reduction of IL-17 cytokine secretion. It showed high exposure in skin, but low in plasma. Topical application of SHR168442 in Vaseline exhibited excellent efficacy in the imiquimod-induced and IL-23-induced psoriasis-like skin inflammation mouse models and correlated with the reduction of Th17 pathway cytokines, IL-6, TNFα and IL-17A. This work demonstrated restricted skin exposure of RORγ antagonist may provide a new topical treatment option as targeted therapeutics for mild to moderate psoriasis patients and may be suitable for the treatment of any other inflammatory disorders that are accessible locally.

Progress toward B-Cell Lymphoma 6 BTB Domain Inhibitors for the Treatment of Diffuse Large B-Cell Lymphoma and Beyond

B-cell lymphoma 6 (BCL6) is a master regulator of germinal center formation that produce antibody-secreting plasma cells and memory B-cells for sustained immune responses. The BTB domain of BCL6 (BCL6^BTB) forms a homodimer that mediates transcriptional repression by recruiting its corepressor proteins to form a biologically functional transcriptional complex. The protein-protein interaction (PPI) between the BCL6^BTB and its corepressors has emerged as a therapeutic target for the treatment of DLBCL and a number of other human cancers. This Perspective provides an overview of recent advances in the development of BCL6^BTB inhibitors from reversible inhibitors, irreversible inhibitors, to BCL6 degraders. Inhibitor design and medicinal chemistry strategies for the development of novel compounds will be provided. The binding mode of new inhibitors to BCL6^BTB are highlighted. Also, the in vitro and in vivo assays used for the evaluation of new compounds will be discussed.

Structural Basis for Pore Blockade of the Human Cardiac Sodium Channel Nav 1.5 by the Antiarrhythmic Drug Quinidine*

Na_v 1.5, the primary voltage-gated Na⁺ (Na_v ) channel in heart, is a major target for class I antiarrhythmic agents. Here we present the cryo-EM structure of full-length human Na_v 1.5 bound to quinidine, a class Ia antiarrhythmic drug, at 3.3 Å resolution. Quinidine is positioned right beneath the selectivity filter in the pore domain and coordinated by residues from repeats I, III, and IV. Pore blockade by quinidine is achieved through both direct obstruction of the ion permeation path and induced rotation of an invariant Tyr residue that tightens the intracellular gate. Structural comparison with a truncated rat Na_v 1.5 in the presence of flecainide, a class Ic agent, reveals distinct binding poses for the two antiarrhythmics within the pore domain. Our work reported here, along with previous studies, reveals the molecular basis for the mechanism of action of class I antiarrhythmic drugs.

Profiling the Tox21 Chemical Collection for Acetylcholinesterase Inhibition

BACKGROUND

Inhibition of acetylcholinesterase (AChE), a biomarker of organophosphorous and carbamate exposure in environmental and occupational human health, has been commonly used to identify potential safety liabilities. So far, many environmental chemicals, including drug candidates, food additives, and industrial chemicals, have not been thoroughly evaluated for their inhibitory effects on AChE activity. AChE inhibitors can have therapeutic applications (e.g., tacrine and donepezil) or neurotoxic consequences (e.g., insecticides and nerve agents).

OBJECTIVES

The objective of the current study was to identify environmental chemicals that inhibit AChE activity using in vitro and in silico models.

METHODS

To identify AChE inhibitors rapidly and efficiently, we have screened the Toxicology in the 21st Century (Tox21) 10K compound library in a quantitative high-throughput screening (qHTS) platform by using the homogenous cell-based AChE inhibition assay and enzyme-based AChE inhibition assays (with or without microsomes). AChE inhibitors identified from the primary screening were further tested in monolayer or spheroid formed by SH-SY5Y and neural stem cell models. The inhibition and binding modes of these identified compounds were studied with time-dependent enzyme-based AChE inhibition assay and molecular docking, respectively.

RESULTS

A group of known AChE inhibitors, such as donepezil, ambenonium dichloride, and tacrine hydrochloride, as well as many previously unreported AChE inhibitors, such as chelerythrine chloride and cilostazol, were identified in this study. Many of these compounds, such as pyrazophos, phosalone, and triazophos, needed metabolic activation. This study identified both reversible (e.g., donepezil and tacrine) and irreversible inhibitors (e.g., chlorpyrifos and bromophos-ethyl). Molecular docking analyses were performed to explain the relative inhibitory potency of selected compounds.

CONCLUSIONS

Our tiered qHTS approach allowed us to generate a robust and reliable data set to evaluate large sets of environmental compounds for their AChE inhibitory activity. https://doi.org/10.1289/EHP6993.

Probes for Photoaffinity Labelling of Kinases

Protein kinases, one of the largest enzyme superfamilies, regulate many physiological and pathological processes. They are drug targets for multiple human diseases, including various cancer types. Probes for the photoaffinity labelling of kinases are important research tools for the study of members of this enzyme superfamily. In this review, we discuss the design principles of these probes, which are mainly derived from inhibitors targeting the ATP pocket. Overall, insights from crystal structures guide the placement of photoreactive groups and detection tags. This has resulted in a wide variety of probes, of which we provide a comprehensive overview. We also discuss several areas of application of these probes, including the identification of targets and off-targets of kinase inhibitors, mapping of their binding sites, the development of inhibitor screening assays, the imaging of kinases, and identification of protein binding partners.

Alternative strategies in cardiac preclinical research and new clinical trial formats

An efficient and safe drug development process is crucial for the establishment of new drugs on the market aiming to increase quality of life and life-span of our patients. Despite technological advances in the past decade, successful launches of drug candidates per year remain low. We here give an overview about some of these advances and suggest improvements for implementation to boost preclinical and clinical drug development with a focus on the cardiovascular field. We highlight advantages and disadvantages of animal experimentation and thoroughly review alternatives in the field of three-dimensional cell culture as well as preclinical use of spheroids and organoids. Microfluidic devices and their potential as organ-on-a-chip systems, as well as the use of living animal and human cardiac tissues are additionally introduced. In the second part, we examine recent gold standard randomized clinical trials and present possible modifications to increase lead candidate throughput: adaptive designs, master protocols, and drug repurposing. In silico and N-of-1 trials have the potential to redefine clinical drug candidate evaluation. Finally, we briefly discuss clinical trial designs during pandemic times.

Discovery of : A Novel Potent and Peripherally Preferred Melatonin Receptor Agonist that Reduces Liver Triglycerides and Steatosis in Diet-Induced Obese Rats

The modulation of melatonin signaling in peripheral tissues holds promise for treating metabolic diseases like obesity, diabetes, and nonalcoholic steatohepatitis. Here, several benzimidazole derivatives have been identified as novel agonists of the melatonin receptors MT1 and MT2. The lead compounds 10b, 15a, and 19a demonstrated subnanomolar potency at MT1/MT2 receptors, high oral bioavailability in rodents, peripherally preferred exposure, and excellent selectivity in a broad panel of targets. Two-month oral administration of 10b in high-fat diet rats led to a reduction in body weight gain similar to dapagliflozin with superior results on hepatic steatosis and triglyceride levels. An early toxicological assessment indicated that 10b (also codified as ACH-000143) was devoid of hERG binding, genotoxicity, and behavioral alterations at doses up to 100 mg/kg p.o., supporting further investigation of this compound as a drug candidate.

Computational Selectivity Assessment of Protease Inhibitors against SARS-CoV-2

The pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a serious global health threat. Since no specific therapeutics are available, researchers around the world screened compounds to inhibit various molecular targets of SARS-CoV-2 including its main protease (Mpro) essential for viral replication. Due to the high urgency of these discovery efforts, off-target binding, which is one of the major reasons for drug-induced toxicity and safety-related drug attrition, was neglected. Here, we used molecular docking, toxicity profiling, and multiple molecular dynamics (MD) protocols to assess the selectivity of 33 reported non-covalent inhibitors of SARS-CoV-2 Mpro against eight proteases and 16 anti-targets. The panel of proteases included SARS-CoV Mpro, cathepsin G, caspase-3, ubiquitin carboxy-terminal hydrolase L1 (UCHL1), thrombin, factor Xa, chymase, and prostasin. Several of the assessed compounds presented considerable off-target binding towards the panel of proteases, as well as the selected anti-targets. Our results further suggest a high risk of off-target binding to chymase and cathepsin G. Thus, in future discovery projects, experimental selectivity assessment should be directed toward these proteases. A systematic selectivity assessment of SARS-CoV-2 Mpro inhibitors, as we report it, was not previously conducted.

β2 -Adrenoceptor agonist activity of higenamine

Higenamine was included in the World Anti-Doping Agency (WADA) Prohibited Substances and Methods List as a β2 -adrenoceptor agonist in 2017, thereby resulting in its prohibition both in and out of competition. The present mini review describes the physiology and pharmacology of adrenoceptors, summarizes the literature addressing the mechanism of action of higenamine and extends these findings with previously unpublished in silico and in vitro work. Studies conducted in isolated in vitro systems, whole-animal preparations and a small number of clinical studies suggest that higenamine acts in part as a β2 -adrenoceptor agonist. In silico predictive tools indicated that higenamine and possibly a metabolite have a high probability of interacting with the β2 -receptor as an agonist. Stable expression of human β2 -receptors in Chinese hamster ovary (CHO) cells to measure agonist activity not only confirmed the activity of higenamine at β2 but also closely agreed with the in silico prediction of potency for this compound. These data confirm and extend literature findings supporting the inclusion of higenamine in the Prohibited List.

Image-based profiling for drug discovery: due for a machine-learning upgrade?

Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug's activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.

Open Targets Platform: supporting systematic drug-target identification and prioritisation

The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowledgebase and user interface to aid systematic target identification and prioritisation for drug discovery based upon underlying evidence. It is publicly available and the underlying code is open source. Since our last update two years ago, we have had 10 releases to maintain and continuously improve evidence for target-disease relationships from 20 different data sources. In addition, we have integrated new evidence from key datasets, including prioritised targets identified from genome-wide CRISPR knockout screens in 300 cancer models (Project Score), and GWAS/UK BioBank statistical genetic analysis evidence from the Open Targets Genetics Portal. We have evolved our evidence scoring framework to improve target identification. To aid the prioritisation of targets and inform on the potential impact of modulating a given target, we have added evaluation of post-marketing adverse drug reactions and new curated information on target tractability and safety. We have also developed the user interface and backend technologies to improve performance and usability. In this article, we describe the latest enhancements to the Platform, to address the fundamental challenge that developing effective and safe drugs is difficult and expensive.

Spirocycle MmpL3 Inhibitors with Improved hERG and Cytotoxicity Profiles as Inhibitors of Mycobacterium tuberculosis Growth

With the emergence of multi-drug-resistant strains of Mycobacterium tuberculosis, there is a pressing need for new oral drugs with novel mechanisms of action. A number of scaffolds with potent anti-tubercular in vitro activity have been identified from phenotypic screening that appear to target MmpL3. However, the scaffolds are typically lipophilic, which facilitates partitioning into hydrophobic membranes, and several contain basic amine groups. Highly lipophilic basic amines are typically cytotoxic against mammalian cell lines and have associated off-target risks, such as inhibition of human ether-à-go-go related gene (hERG) and IKr potassium current modulation. The spirocycle compound 3 was reported to target MmpL3 and displayed promising efficacy in a murine model of acute tuberculosis (TB) infection. However, this highly lipophilic monobasic amine was cytotoxic and inhibited the hERG ion channel. Herein, the related spirocycles (1-2) are described, which were identified following phenotypic screening of the Eli Lilly corporate library against M. tuberculosis. The novel N-alkylated pyrazole portion offered improved physicochemical properties, and optimization led to identification of a zwitterion series, exemplified by lead 29, with decreased HepG2 cytotoxicity as well as limited hERG ion channel inhibition. Strains with mutations in MmpL3 were resistant to 29, and under replicating conditions, 29 demonstrated bactericidal activity against M. tuberculosis. Unfortunately, compound 29 had no efficacy in an acute model of TB infection; this was most likely due to the in vivo exposure remaining above the minimal inhibitory concentration for only a limited time.

Parallel Recordings of Transmembrane hERG Channel Currents Based on Solvent-Free Lipid Bilayer Microarray

The reconstitution of ion-channel proteins in artificially formed bilayer lipid membranes (BLMs) forms a well-defined system for the functional analysis of ion channels and screening of the effects of drugs that act on these proteins. To improve the efficiency of the BLM reconstitution system, we report on a microarray of stable solvent-free BLMs formed in microfabricated silicon (Si) chips, where micro-apertures with well-defined nano- and micro-tapered edges were fabricated. Sixteen micro-wells were manufactured in a chamber made of Teflon^®, and the Si chips were individually embedded in the respective wells as a recording site. Typically, 11 to 16 BLMs were simultaneously formed with an average BLM number of 13.1, which corresponded to a formation probability of 82%. Parallel recordings of ion-channel activities from multiple BLMs were successfully demonstrated using the human ether-a-go-go-related gene (hERG) potassium channel, of which the relation to arrhythmic side effects following drug treatment is well recognized.

Novel Sigma 1 Receptor Antagonists as Potential Therapeutics for Pain Management

The sigma 1 receptor (S1R) is a molecular chaperone protein located in the endoplasmic reticulum and plasma membranes and has been shown to play important roles in various pathological disorders including pain and, as recently discovered, COVID-19. Employing structure- and QSAR-based drug design strategies, we rationally designed, synthesized, and biologically evaluated a series of novel triazole-based S1R antagonists. Compound 10 exhibited potent binding affinity for S1R, high selectivity over S2R and 87 other human targets, acceptable in vitro metabolic stability, slow clearance in liver microsomes, and excellent blood-brain barrier permeability in rats. Further in vivo studies in rats showed that 10 exhibited negligible acute toxicity in the rotarod test and statistically significant analgesic effects in the formalin test for acute inflammatory pain and paclitaxel-induced neuropathic pain models during cancer chemotherapy. These encouraging results promote further development of our triazole-based S1R antagonists as novel treatments for pain of different etiologies.

Survey of Similarity-Based Prediction of Drug-Protein Interactions

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models

In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro methods together with other computational methods such as quantitative structure-activity relationship modeling and absorption, distribution, metabolism, and excretion calculations are being used. An overview of machine learning and its applications in predictive toxicology is presented here, including support vector machines (SVMs), random forest (RF) and decision trees (DTs), neural networks, regression models, naïve Bayes, k-nearest neighbors, and ensemble learning. The recent successes of these machine learning methods in predictive toxicology are summarized, and a comparison of some models used in predictive toxicology is presented. In predictive toxicology, SVMs, RF, and DTs are the dominant machine learning methods due to the characteristics of the data available. Lastly, this review describes the current challenges facing the use of machine learning in predictive toxicology and offers insights into the possible areas of improvement in the field.

Systematic Analysis of Protein Targets Associated with Adverse Events of Drugs from Clinical Trials and Postmarketing Reports

Adverse drug reactions (ADRs) are undesired effects of medicines that can harm patients and are a significant source of attrition in drug development. ADRs are anticipated by routinely screening drugs against secondary pharmacology protein panels. However, there is still a lack of quantitative information on the links between these off-target proteins and the reporting of ADRs in humans. Here, we present a systematic analysis of associations between measured and predicted in vitro bioactivities of drugs and adverse events (AEs) in humans from two sources of data: the Side Effect Resource, derived from clinical trials, and the Food and Drug Administration Adverse Event Reporting System, derived from postmarketing surveillance. The ratio of a drug's therapeutic unbound plasma concentration over the drug's in vitro potency against a given protein was used to select proteins most likely to be relevant to in vivo effects. In examining individual target bioactivities as predictors of AEs, we found a trade-off between the positive predictive value and the fraction of drugs with AEs that can be detected. However, considering sets of multiple targets for the same AE can help identify a greater fraction of AE-associated drugs. Of the 45 targets with statistically significant associations to AEs, 30 are included on existing safety target panels. The remaining 15 targets include 9 carbonic anhydrases, of which CA5B is significantly associated with cholestatic jaundice. We include the full quantitative data on associations between measured and predicted in vitro bioactivities and AEs in humans in this work, which can be used to make a more informed selection of safety profiling targets.

Confidence in Inactive and Active Predictions from Structural Alerts

Having a measure of confidence in computational predictions of biological activity from in silico tools is vital when making predictions for new chemicals, for example, in chemical risk assessment. Where predictions of biological activity are used as an indicator of a potential hazard, false-negative predictions are the most concerning prediction; however, assigning confidence in inactive predictions is particularly challenging. How can one confidently identify the absence of activating features? In this study, we present methods for assigning confidence to both active and inactive predictions from structural alerts for protein-binding molecular initiating events (MIEs). Structural alerts were derived through an iterative statistical method. Confidence in the activity predictions is assigned by measuring the Tanimoto similarity between Morgan fingerprints of chemicals in the test set to relevant chemicals in the training set, and suitable cutoff values have been defined to give different confidence categories. To avoid a potential compound series bias in the test set and hence overestimate the performance of the method, we measured the biological activity of 27 compounds with 24 proteins, which gave us an additional 648 experimental measurements; many of the measurements are currently nonexistent in the literature and databases. This data set was complemented with newly measured biological activities published in ChEMBL25 and formed a combined independent validation data set. Applying the confidence categories to the computational predictions for the new data leads to the identification of chemicals for which one should be confident of either an inactive or active prediction, allowing model predictions to be used responsibly.

High-Throughput Screening to Identify Chemical Cardiotoxic Potential

Cardiovascular (CV) disease is one of the most prevalent public health concerns, and mounting evidence supports the contribution of environmental chemicals to CV disease burden. In this study, we performed cardiotoxicity profiling for the Tox21 chemical library by focusing on high-throughput screening (HTS) assays whose targets are associated with adverse events related to CV failure modes. Our objective was to develop new hypotheses around environmental chemicals of potential interest for adverse CV outcomes using Tox21/ToxCast HTS data. Molecular and cellular events linked to six failure modes of CV toxicity were cross-referenced with 1399 Tox21/ToxCast assays to identify cardio-relevant bioactivity signatures. The resulting 40 targets, measured in 314 assays, were integrated via a ToxPi visualization tool and ranking system to prioritize 1138 chemicals based upon formal integration across multiple domains of information. Filtering was performed based on cytotoxicity and generalized cell stress endpoints to try and isolate chemicals with effects specific to CV biology, and bioactivity- and structure-based clustering identified subgroups of chemicals preferentially affecting targets such as ion channels and vascular tissue biology. Our approach identified drugs with known cardiotoxic effects, such as estrogenic modulators like clomiphene and raloxifene, anti-arrhythmic drugs like amiodarone and haloperidol, and antipsychotic drugs like chlorpromazine. Several classes of environmental chemicals such as organotins, bisphenol-like chemicals, pesticides, and quaternary ammonium compounds demonstrated strong bioactivity against CV targets; these were compared to existing data in the literature (e.g., from cardiomyocytes, animal data, or human epidemiological studies) and prioritized for further testing.

New Associations between Drug-Induced Adverse Events in Animal Models and Humans Reveal Novel Candidate Safety Targets

To improve our ability to extrapolate preclinical toxicity to humans, there is a need to understand and quantify the concordance of adverse events (AEs) between animal models and clinical studies. In the present work, we discovered 3011 statistically significant associations between preclinical and clinical AEs caused by drugs reported in the PharmaPendium database of which 2952 were new associations between toxicities encoded by different Medical Dictionary for Regulatory Activities terms across species. To find plausible and testable candidate off-target drug activities for the derived associations, we investigated the genetic overlap between the genes linked to both a preclinical and a clinical AE and the protein targets found to interact with one or more drugs causing both AEs. We discuss three associations from the analysis in more detail for which novel candidate off-target drug activities could be identified, namely, the association of preclinical mutagenicity readouts with clinical teratospermia and ovarian failure, the association of preclinical reflexes abnormal with clinical poor-quality sleep, and the association of preclinical psychomotor hyperactivity with clinical drug withdrawal syndrome. Our analysis successfully identified a total of 77% of known safety targets currently tested in in vitro screening panels plus an additional 431 genes which were proposed for investigation as future safety targets for different clinical toxicities. This work provides new translational toxicity relationships beyond AE term-matching, the results of which can be used for risk profiling of future new chemical entities for clinical studies and for the development of future in vitro safety panels.

Advances in Multidimensional Cardiac Biosensing Technologies: From Electrophysiology to Mechanical Motion and Contractile Force

Cardiovascular disease is currently a leading killer to human, while drug-induced cardiotoxicity remains the main cause of the withdrawal and attrition of drugs. Taking clinical correlation and throughput into account, cardiomyocyte is perfect as in vitro cardiac model for heart disease modeling, drug discovery, and cardiotoxicity assessment by accurately measuring the physiological multiparameters of cardiomyocytes. Remarkably, cardiomyocytes present both electrophysiological and biomechanical characteristics due to the unique excitation-contraction coupling, which plays a significant role in studying the cardiomyocytes. This review mainly focuses on the recent advances of biosensing technologies for the 2D and 3D cardiac models with three special properties: electrophysiology, mechanical motion, and contractile force. These high-performance multidimensional cardiac models are popular and effective to rebuild and mimic the heart in vitro. To help understand the high-quality and accurate physiologies, related detection techniques are highly demanded, from microtechnology to nanotechnology, from extracellular to intracellular recording, from multiple cells to single cell, and from planar to 3D models. Furthermore, the characteristics, advantages, limitations, and applications of these cardiac biosensing technologies, as well as the future development prospects should contribute to the systematization and expansion of knowledge.

Metallodrugs are unique: opportunities and challenges of discovery and development

Metals play vital roles in nutrients and medicines and provide chemical functionalities that are not accessible to purely organic compounds. At least 10 metals are essential for human life and about 46 other non-essential metals (including radionuclides) are also used in drug therapies and diagnostic agents. These include platinum drugs (in 50% of cancer chemotherapies), lithium (bipolar disorders), silver (antimicrobials), and bismuth (broad-spectrum antibiotics). While the quest for novel and better drugs is now as urgent as ever, drug discovery and development pipelines established for organic drugs and based on target identification and high-throughput screening of compound libraries are less effective when applied to metallodrugs. Metallodrugs are often prodrugs which undergo activation by ligand substitution or redox reactions, and are multi-targeting, all of which need to be considered when establishing structure-activity relationships. We focus on early-stage in vitro drug discovery, highlighting the challenges of evaluating anticancer, antimicrobial and antiviral metallo-pharmacophores in cultured cells, and identifying their targets. We highlight advances in the application of metal-specific techniques that can assist the preclinical development, including synchrotron X-ray spectro(micro)scopy, luminescence, and mass spectrometry-based methods, combined with proteomic and genomic (metallomic) approaches. A deeper understanding of the behavior of metals and metallodrugs in biological systems is not only key to the design of novel agents with unique mechanisms of action, but also to new understanding of clinically-established drugs.

Proposed Allosteric Inhibitors Bind to the ATP Site of CK2α

CK2α is a ubiquitous, well-studied kinase that is a target for small-molecule inhibition, for treatment of cancers. While many different classes of adenosine 5'-triphosphate (ATP)-competitive inhibitors have been described for CK2α, they tend to suffer from significant off-target activity and new approaches are needed. A series of inhibitors of CK2α has recently been described as allosteric, acting at a previously unidentified binding site. Given the similarity of these inhibitors to known ATP-competitive inhibitors, we have investigated them further. In our thorough structural and biophysical analyses, we have found no evidence that these inhibitors bind to the proposed allosteric site. Rather, we report crystal structures, competitive isothermal titration calorimetry (ITC) and NMR, hydrogen-deuterium exchange (HDX) mass spectrometry, and chemoinformatic analyses that all point to these compounds binding in the ATP pocket. Comparisons of our results and experimental approach with the data presented in the original report suggest that the primary reason for the disparity is nonspecific inhibition by aggregation.

The Effects of Single Nucleotide Polymorphisms in Cancer RNAi Therapies

Tremendous progress in RNAi delivery methods and design has allowed for the effective development of siRNA-based therapeutics that are currently under clinical investigation for various cancer treatments. This approach has the potential to revolutionize cancer therapy by providing the ability to specifically downregulate or upregulate the mRNA of any protein of interest. This exquisite specificity, unfortunately, also has a downside. Genetic variations in the human population are common because of the presence of single nucleotide polymorphisms (SNPs). SNPs lead to synonymous and non-synonymous changes and they occur once in every 300 base pairs in both coding and non-coding regions in the human genome. Much less common are the somatic mosaicism variations associated with genetically distinct populations of cells within an individual that is derived from postzygotic mutations. These heterogeneities in the population can affect the RNAi's efficacy or more problematically, which can lead to unpredictable and sometimes adverse side effects. From a more positive viewpoint, both SNPs and somatic mosaicisms have also been implicated in human diseases, including cancer, and these specific changes could offer the ability to effectively and, more importantly, selectively target the cancer cells. In this review, we discuss how SNPs in the human population can influence the development and success of novel anticancer RNAi therapies and the importance of why SNPs should be carefully considered.

Predicting target profiles with confidence as a service using docking scores

Background

Identifying and assessing ligand-target binding is a core component in early drug discovery as one or more unwanted interactions may be associated with safety issues.

Contributions

We present an open-source, extendable web service for predicting target profiles with confidence using machine learning for a panel of 7 targets, where models are trained on molecular docking scores from a large virtual library. The method uses conformal prediction to produce valid measures of prediction efficiency for a particular confidence level. The service also offers the possibility to dock chemical structures to the panel of targets with QuickVina on individual compound basis.

Results

The docking procedure and resulting models were validated by docking well-known inhibitors for each of the 7 targets using QuickVina. The model predictions showed comparable performance to molecular docking scores against an external validation set. The implementation as publicly available microservices on Kubernetes ensures resilience, scalability, and extensibility.

Rational design of balanced dual-targeting antibiotics with limited resistance

Antibiotics that inhibit multiple bacterial targets offer a promising therapeutic strategy against resistance evolution, but developing such antibiotics is challenging. Here we demonstrate that a rational design of balanced multitargeting antibiotics is feasible by using a medicinal chemistry workflow. The resultant lead compounds, ULD1 and ULD2, belonging to a novel chemical class, almost equipotently inhibit bacterial DNA gyrase and topoisomerase IV complexes and interact with multiple evolutionary conserved amino acids in the ATP-binding pockets of their target proteins. ULD1 and ULD2 are excellently potent against a broad range of gram-positive bacteria. Notably, the efficacy of these compounds was tested against a broad panel of multidrug-resistant Staphylococcus aureus clinical strains. Antibiotics with clinical relevance against staphylococcal infections fail to inhibit a significant fraction of these isolates, whereas both ULD1 and ULD2 inhibit all of them (minimum inhibitory concentration [MIC] ≤1 μg/mL). Resistance mutations against these compounds are rare, have limited impact on compound susceptibility, and substantially reduce bacterial growth. Based on their efficacy and lack of toxicity demonstrated in murine infection models, these compounds could translate into new therapies against multidrug-resistant bacterial infections.

Large-scale RNAi screening uncovers therapeutic targets in the parasite Schistosoma mansoni

Schistosome parasites kill 250,000 people every year. Treatment of schistosomiasis relies on the drug praziquantel. Unfortunately, a scarcity of molecular tools has hindered the discovery of new drug targets. Here, we describe a large-scale RNA interference (RNAi) screen in adult Schistosoma mansoni that examined the function of 2216 genes. We identified 261 genes with phenotypes affecting neuromuscular function, tissue integrity, stem cell maintenance, and parasite survival. Leveraging these data, we prioritized compounds with activity against the parasites and uncovered a pair of protein kinases (TAO and STK25) that cooperate to maintain muscle-specific messenger RNA transcription. Loss of either of these kinases results in paralysis and worm death in a mammalian host. These studies may help expedite therapeutic development and invigorate studies of these neglected parasites.

Neurotropic activity and safety of methylene-cycloalkylacetate (MCA) derivative 3-(3-allyl-2-methylenecyclohexyl) propanoic acid

Polyneuropathy is a disease involving multiple peripheral nerves injuries. Axon regrowth remains the major prerequisite for plasticity, regeneration, circuit formation, and eventually functional recovery and therefore, regulation of neurite outgrowth might be a candidate for treating polyneuropathies. In a recent study, we synthesized and established the methylene-cycloalkylacetate (MCAs) pharmacophore as a lead for the development of a neurotropic drug (inducing neurite/axonal outgrowth) using the PC12 neuronal model. In the present study we extended the characterizations of the in vitro neurotropic effect of the derivative 3-(3-allyl-2-methylenecyclohexyl) propanoic acid (MCA-13) on dorsal root ganglia and spinal cord neuronal cultures and analyzed its safety properties using blood biochemistry and cell counting, acute toxicity evaluation in mice and different in vitro "off-target" pharmacological evaluations. This MCA derivative deserves further preclinical mechanistic pharmacological characterizations including therapeutic efficacy in in vivo animal models of polyneuropathies, toward development of a clinically relevant neurotropic drug.

Identification and Optimization of a Series of 8-Hydroxy Naphthyridines with Potent In Vitro Antileishmanial Activity: Initial SAR and Assessment of In Vivo Activity

Visceral leishmaniasis (VL) is a parasitic infection that results in approximately 26 000–65 000 deaths annually. The available treatments are hampered by issues such as toxicity, variable efficacy, and unsuitable dosing options. The need for new treatments is urgent and led to a collaboration between the Drugs for Neglected Diseases initiative (DNDi), GlaxoSmithKline (GSK), and the University of Dundee. An 8-hydroxynaphthyridine was identified as a start point, and an early compound demonstrated weak efficacy in a mouse model of VL but was hampered by glucuronidation. Efforts to address this led to the development of compounds with improved in vitro profiles, but these were poorly tolerated in vivo. Investigation of the mode of action (MoA) demonstrated that activity was driven by sequestration of divalent metal cations, a mechanism which was likely to drive the poor tolerability. This highlights the importance of investigating MoA and pharmacokinetics at an early stage for phenotypically active series.

The activities of drug inactive ingredients on biological targets

Excipients, considered "inactive ingredients," are a major component of formulated drugs and play key roles in their pharmacokinetics. Despite their pervasiveness, whether they are active on any targets has not been systematically explored. We computed the likelihood that approved excipients would bind to molecular targets. Testing in vitro revealed 25 excipient activities, ranging from low-nanomolar to high-micromolar concentration. Another 109 activities were identified by testing against clinical safety targets. In cellular models, five excipients had fingerprints predictive of system-level toxicity. Exposures of seven excipients were investigated, and in certain populations, two of these may reach levels of in vitro target potency, including brain and gut exposure of thimerosal and its major metabolite, which had dopamine D3 receptor dissociation constant K _d values of 320 and 210 nM, respectively. Although most excipients deserve their status as inert, many approved excipients may directly modulate physiologically relevant targets.

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

We present a supercomputer-driven pipeline for in-silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. We also describe preliminary results obtained for 23 systems involving eight protein targets of the proteome of SARS CoV-2. THe MD performed is temperature replica-exchange enhanced sampling, making use of the massively parallel supercomputing on the SUMMIT supercomputer at Oak Ridge National Laboratory, with which more than 1ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to ten configurations of each of the 23 SARS CoV-2 systems using AutoDock Vina. We also demonstrate that using Autodock-GPU on SUMMIT, it is possible to perform exhaustive docking of one billion compounds in under 24 hours. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and AI methods to cluster MD trajectories and rescore docking poses.

Optical Interrogation of Sympathetic Neuronal Effects on Macroscopic Cardiomyocyte Network Dynamics

Cardiac stimulation via sympathetic neurons can potentially trigger arrhythmias. We present approaches to study neuron-cardiomyocyte interactions involving optogenetic selective probing and all-optical electrophysiology to measure activity in an automated fashion. Here we demonstrate the utility of optical interrogation of sympathetic neurons and their effects on macroscopic cardiomyocyte network dynamics to address research targets such as the effects of adrenergic stimulation via the release of neurotransmitters, the effect of neuronal numbers on cardiac behavior, and the applicability of optogenetics in mechanistic in vitro studies. As arrhythmias are emergent behaviors that involve the coordinated activity of millions of cells, we image at macroscopic scales to capture complex dynamics. We show that neurons can both decrease and increase wave stability and re-entrant activity in culture depending on their induced activity-a finding that may help us understand the often conflicting results seen in experimental and clinical studies.

Inhibition of natriuretic peptide receptor 1 reduces itch in mice

There is a major clinical need for new therapies for the treatment of chronic itch. Many of the molecular components involved in itch neurotransmission are known, including the neuropeptide NPPB, a transmitter required for normal itch responses to multiple pruritogens in mice. Here, we investigated the potential for a novel strategy for the treatment of itch that involves the inhibition of the NPPB receptor NPR1 (natriuretic peptide receptor 1). Because there are no available effective human NPR1 (hNPR1) antagonists, we performed a high-throughput cell-based screen and identified 15 small-molecule hNPR1 inhibitors. Using in vitro assays, we demonstrated that these compounds specifically inhibit hNPR1 and murine NPR1 (mNPR1). In vivo, NPR1 antagonism attenuated behavioral responses to both acute itch- and chronic itch-challenged mice. Together, our results suggest that inhibiting NPR1 might be an effective strategy for treating acute and chronic itch.

Preclinical characterization of CPL302-253, a selective inhibitor of PI3Kδ, as the candidate for the inhalatory treatment and prevention of Asthma

Asthma is a common chronic inflammatory disease. Although effective asthma therapies are available, part of asthmatic population do not respond to these treatment options. In this work we present the result of development of CPL302-253 molecule, a selective PI3Kδ inhibitor. This molecule is intended to be a preclinical candidate for dry powder inhalation in asthma treatment. Studies we performed showed that this molecule is safe and effective PI3Kδ inhibitor that can impact many immune functions. We developed a short, 15-day HDM induced asthma mouse model, in which we showed that CPL302-253 is able to block inflammatory processes leading to asthma development in vivo.

Translation of in vitro cannabinoid 1 receptor agonist activity to in vivo pharmacodynamic endpoints

INTRODUCTION

Building an understanding of in vivo efficacy based on the evaluation of in vitro affinity or potency is critical in expediting early decision making in drug discovery and can significantly reduce the need for animal studies. The aim of the present study was to understand the translation of in vitro to in vivo endpoints for the cannabinoid receptor 1 (CB1).

METHODS

Using a selection of CB1 agonists we describe an evaluation of in vitro to in vivo translation comparing in vitro receptor affinity or functional potency, using both cAMP and β-arrestin endpoints, to various in vivo CB1 agonist-associated endpoints.

RESULTS

We demonstrate that in vitro CB1 agonism significantly correlates with the CB1-induced cue in the drug discrimination model in vivo, but not with other purported CB1 agonist-mediated in vivo endpoints, including hypothermia and sedation. Thus, these data challenge common perceptions regarding CB1 agonist-induced tetrad effects in rodents.

DISCUSSION

This work exemplifies how in vitro profiling of receptor affinity or potency can predict in vivo pharmacodynamic effects, using the CB1 as an example system. The translatability of in vitro activity to in vivo efficacy allows for the ability to rapidly contextualize off-target CB1 in vitro findings, allowing clear and rapid definition of the risk posed by such activity without the need for extensive animal studies. This has significant implications in terms of early decision making in drug discovery and reducing the use of animals in research, while also outlining a template for expanding the approach for additional targets.

New insights for screening etomidate analogues in the human H295R cell model

Etomidate is a sedative-hypnotic with excellent pharmacological effects, including rapid onset and hemodynamic stability. However, etomidate causes adrenocortical toxicity via binding to 11β-hydroxylase. Therefore, developing an approach to screen new etomidate analogues without endocrine-disrupting effects is urgently warranted. In this study, we employed the adrenocortical tumour cell line, NCI-H295R, as an in vitro system for etomidate analogues screening and detected the hormone levels in these cells using a high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method. After obtaining the concentration-response curves of hormone release, the "Adrenocortical Inhibitory Index" was used to evaluate the adrenocortical inhibitory potency of each compound. In summary, we demonstrate the benefits of our methods for screening of etomidate analogues that lack adrenocortical suppression, especially when this in vitro system is combined with in vivo testing.

Incorporation of in vitro techniques for botanicals dietary supplement safety assessment - Towards evaluation of developmental and reproductive toxicity (DART)

As complex mixtures, botanicals present unique challenges when assessing safe use, particularly when endpoint gaps exist that cannot be fully resolved by existing toxicological literature. Here we explore in vitro gene expression as well receptor binding and enzyme activity as alternative assays to inform on developmental and reproductive toxicity (DART) relevant modes of action, since DART data gaps are common for botanicals. Specifically, botanicals suspected to have DART effects, in addition to those with a significant history of use, were tested in these assays. Gene expression changes in a number of different cell types were analysed using the connectivity mapping approach (CMap) to identify modes of action through a functional read across approach. Taken together with ligand affinity data obtained using a set of molecular targets customised towards known DART relevant modes of action, it was possible to inform DART risk using functional analogues, potency comparisons and a margin of internal exposure approach.

Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology

BACKGROUND

Adverse drug reactions (ADRs) are one of the leading causes of morbidity and mortality in health care. Understanding which drug targets are linked to ADRs can lead to the development of safer medicines.

METHODS

Here, we analyse in vitro secondary pharmacology of common (off) targets for 2134 marketed drugs. To associate these drugs with human ADRs, we utilized FDA Adverse Event Reports and developed random forest models that predict ADR occurrences from in vitro pharmacological profiles.

FINDINGS

By evaluating Gini importance scores of model features, we identify 221 target-ADR associations, which co-occur in PubMed abstracts to a greater extent than expected by chance. Amongst these are established relations, such as the association of in vitro hERG binding with cardiac arrhythmias, which further validate our machine learning approach. Evidence on bile acid metabolism supports our identification of associations between the Bile Salt Export Pump and renal, thyroid, lipid metabolism, respiratory tract and central nervous system disorders. Unexpectedly, our model suggests PDE3 is associated with 40 ADRs.

INTERPRETATION

These associations provide a comprehensive resource to support drug development and human biology studies.

FUNDING

This study was not supported by any formal funding bodies.

Identifying and Characterizing Stress Pathways of Concern for Consumer Safety in Next-Generation Risk Assessment

Many substances for which consumer safety risk assessments need to be conducted are not associated with specific toxicity modes of action, but rather exhibit nonspecific toxicity leading to cell stress. In this work, a cellular stress panel is described, consisting of 36 biomarkers representing mitochondrial toxicity, cell stress, and cell health, measured predominantly using high content imaging. To evaluate the panel, data were generated for 13 substances at exposures consistent with typical use-case scenarios. These included some that have been shown to cause adverse effects in a proportion of exposed humans and have a toxicological mode-of-action associated with cellular stress (eg, doxorubicin, troglitazone, and diclofenac), and some that are not associated with adverse effects due to cellular stress at human-relevant exposures (eg, caffeine, niacinamide, and phenoxyethanol). For each substance, concentration response data were generated for each biomarker at 3 timepoints. A Bayesian model was then developed to quantify the evidence for a biological response, and if present, a credibility range for the estimated point of departure (PoD) was determined. PoDs were compared with the plasma Cmax associated with the typical substance exposures, and indicated a clear differentiation between "low" risk and "high" risk chemical exposure scenarios. Developing robust methods to characterize the in vitro bioactivity of xenobiotics is an important part of non-animal safety assessment. The results presented in this work show that the cellular stress panel can be used, together with other new approach methodologies, to identify chemical exposures that are protective of consumer health.

A Next-Generation Risk Assessment Case Study for Coumarin in Cosmetic Products

Next-Generation Risk Assessment is defined as an exposure-led, hypothesis-driven risk assessment approach that integrates new approach methodologies (NAMs) to assure safety without the use of animal testing. These principles were applied to a hypothetical safety assessment of 0.1% coumarin in face cream and body lotion. For the purpose of evaluating the use of NAMs, existing animal and human data on coumarin were excluded. Internal concentrations (plasma Cmax) were estimated using a physiologically based kinetic model for dermally applied coumarin. Systemic toxicity was assessed using a battery of in vitro NAMs to identify points of departure (PoDs) for a variety of biological effects such as receptor-mediated and immunomodulatory effects (Eurofins SafetyScreen44 and BioMap Diversity 8 Panel, respectively), and general bioactivity (ToxCast data, an in vitro cell stress panel and high-throughput transcriptomics). In addition, in silico alerts for genotoxicity were followed up with the ToxTracker tool. The PoDs from the in vitro assays were plotted against the calculated in vivo exposure to calculate a margin of safety with associated uncertainty. The predicted Cmax values for face cream and body lotion were lower than all PoDs with margin of safety higher than 100. Furthermore, coumarin was not genotoxic, did not bind to any of the 44 receptors tested and did not show any immunomodulatory effects at consumer-relevant exposures. In conclusion, this case study demonstrated the value of integrating exposure science, computational modeling and in vitro bioactivity data, to reach a safety decision without animal data.

Neural network activation similarity: a new measure to assist decision making in chemical toxicology

Deep learning neural networks, constructed for the prediction of chemical binding at 79 pharmacologically important human biological targets, show extremely high performance on test data (accuracy 92.2 ± 4.2%, MCC 0.814 ± 0.093 and ROC-AUC 0.96 ± 0.04). A new molecular similarity measure, Neural Network Activation Similarity, has been developed, based on signal propagation through the network. This is complementary to standard Tanimoto similarity, and the combined use increases confidence in the computer's prediction of activity for new chemicals by providing a greater understanding of the underlying justification. The in silico prediction of these human molecular initiating events is central to the future of chemical safety risk assessment and improves the efficiency of safety decision making.

Leveraging Human Genetics to Identify Safety Signals Prior to Drug Marketing Approval and Clinical Use

INTRODUCTION

When a new drug or biologic product enters the market, its full spectrum of side effects is not yet fully understood, as use in the real world often uncovers nuances not suggested within the relatively narrow confines of preapproval preclinical and trial work.

OBJECTIVE

We describe a new, phenome-wide association study (PheWAS)- and evidence-based approach for detection of potential adverse drug effects.

METHODS

We leveraged our established platform, which integrates human genetic data with associated phenotypes in electronic health records from 29,722 patients of European ancestry, to identify gene-phenotype associations that may represent known safety issues. We examined PheWAS data and the published literature for 16 genes, each of which encodes a protein targeted by at least one drug or biologic product.

RESULTS

Initial data demonstrated that our novel approach (safety ascertainment using PheWAS [SA-PheWAS]) can replicate published safety information across multiple drug classes, with validated findings for 13 of 16 gene-drug class pairs.

CONCLUSIONS

By connecting and integrating in vivo and in silico data, SA-PheWAS offers an opportunity to supplement current methods for predicting or confirming safety signals associated with therapeutic agents.

Novel Approaches and Challenges of Discovery of Exosite Modulators of a Disintegrin and Metalloprotease 10

A disintegrin and metaproteinase 10 is an important target for multiple therapeutic areas, however, despite drug discovery efforts by both industry and academia no compounds have reached the clinic so far. The lack of enzyme and substrate selectivity of developmental drugs is believed to be a main obstacle to the success. In this review, we will focus on novel approaches and associated challenges in discovery of ADAM10 selective modulators that can overcome shortcomings of previous generations of compounds and be translated into the clinic.

Facts, Patterns, and Principles in Drug Discovery: Appraising the Rule of 5 with Measured Physicochemical Data

The rule of 5 was designed to estimate the likelihood of poor absorption or permeation, noting the impact of poor solubility. This Perspective explores the impact of various physicochemical descriptors and contemporary lipophilicity measurements on permeability and solubility, showing that the distribution coefficient log D_7.4 (rather than log P) is the most impactful parameter. Molecular weight, almost invariably the defining characteristic of "beyond the rule of 5" compounds, has little impact on solubility when log D_7.4 measurements and aromaticity are considered. Predicting permeation is more complex, given passive and carrier transport mechanisms; however, notable patterns of behavior are apparent, giving insight even "beyond the rule of 5". Recommended best practices should involve using the facts (measurements) and the patterns they reveal to establish informative principles rather than fastidious rules.