Potential functional and pathological side effects related to off-target pharmacological activity

ICH S1 prospective evaluation study and weight of evidence assessments: commentary from industry representatives

Industry representatives on the ICH S1B(R1) Expert Working Group (EWG) worked closely with colleagues from the Drug Regulatory Authorities to develop an addendum to the ICH S1B guideline on carcinogenicity studies that allows for a weight-of-evidence (WoE) carcinogenicity assessment in some cases, rather than conducting a 2-year rat carcinogenicity study. A subgroup of the EWG composed of regulators have published in this issue a detailed analysis of the Prospective Evaluation Study (PES) conducted under the auspices of the ICH S1B(R1) EWG. Based on the experience gained through the Prospective Evaluation Study (PES) process, industry members of the EWG have prepared the following commentary to aid sponsors in assessing the standard WoE factors, considering how novel investigative approaches may be used to support a WoE assessment, and preparing appropriate documentation of the WoE assessment for presentation to regulatory authorities. The commentary also reviews some of the implementation challenges sponsors must consider in developing a carcinogenicity assessment strategy. Finally, case examples drawn from previously marketed products are provided as a supplement to this commentary to provide additional examples of how WoE criteria may be applied. The information and opinions expressed in this commentary are aimed at increasing the quality of WoE assessments to ensure the successful implementation of this approach.

The state of the art in secondary pharmacology and its impact on the safety of new medicines

Secondary pharmacology screening of investigational small-molecule drugs for potentially adverse off-target activities has become standard practice in pharmaceutical research and development, and regulatory agencies are increasingly requesting data on activity against targets with recognized adverse effect relationships. However, the screening strategies and target panels used by pharmaceutical companies may vary substantially. To help identify commonalities and differences, as well as to highlight opportunities for further optimization of secondary pharmacology assessment, we conducted a broad-ranging survey across 18 companies under the auspices of the DruSafe leadership group of the International Consortium for Innovation and Quality in Pharmaceutical Development. Based on our analysis of this survey and discussions and additional research within the group, we present here an overview of the current state of the art in secondary pharmacology screening. We discuss best practices, including additional safety-associated targets not covered by most current screening panels, and present approaches for interpreting and reporting off-target activities. We also provide an assessment of the safety impact of secondary pharmacology screening, and a perspective on opportunities and challenges in this rapidly developing field.

Developing a pragmatic consensus procedure supporting the ICH S1B(R1) weight of evidence carcinogenicity assessment

The ICH S1B carcinogenicity global testing guideline has been recently revised with a novel addendum that describes a comprehensive integrated Weight of Evidence (WoE) approach to determine the need for a 2-year rat carcinogenicity study. In the present work, experts from different organizations have joined efforts to standardize as much as possible a procedural framework for the integration of evidence associated with the different ICH S1B(R1) WoE criteria. The framework uses a pragmatic consensus procedure for carcinogenicity hazard assessment to facilitate transparent, consistent, and documented decision-making and it discusses best-practices both for the organization of studies and presentation of data in a format suitable for regulatory review. First, it is acknowledged that the six WoE factors described in the addendum form an integrated network of evidence within a holistic assessment framework that is used synergistically to analyze and explain safety signals. Second, the proposed standardized procedure builds upon different considerations related to the primary sources of evidence, mechanistic analysis, alternative methodologies and novel investigative approaches, metabolites, and reliability of the data and other acquired information. Each of the six WoE factors is described highlighting how they can contribute evidence for the overall WoE assessment. A suggested reporting format to summarize the cross-integration of evidence from the different WoE factors is also presented. This work also notes that even if a 2-year rat study is ultimately required, creating a WoE assessment is valuable in understanding the specific factors and levels of human carcinogenic risk better than have been identified previously with the 2-year rat bioassay alone.

Preclinical cardiovascular safety assessment of pharmacology-toxicology relationship for a set of novel kinase inhibitors

Cardiovascular toxicity is one of the more common causes of attrition in preclinical and clinical drug development. Preclinical cardiovascular safety assessment involves numerous in vitro and in vivo endpoints which are being continually reviewed and improved to lower the incidence of cardiovascular toxicity that manifests only after the initiation of clinical trials. An example of notable preclinical toxicity is necrosis in the papillary muscle of the left ventricle in dogs that is induced by exaggerated pharmacological effects of vasodilators or positive inotropic/vasodilating off-target drug effects. Two distinct, small-molecule inhibitors that target an intracellular kinase, Compound A and Compound B, were profiled in 2-week dose-range finding and 4-week toxicity studies. Serum cardiac troponin (cTnI) was evaluated after a single dose and after 2-week and 4-week repeat dose studies with each kinase inhibitor. Acute effects on hemodynamic (heart rate, blood pressures, left ventricular contractility) and electrocardiographic (QTcV, PR, QRS intervals) endpoints by each inhibitor were assessed in an anesthetized dog cardiovascular model. Cardiovascular degeneration/necrosis with and without fibrosis was observed in dogs and correlated to increases in serum cTnI in repeat-dose toxicity studies. At the same doses used in toxicologic assessments, both kinase inhibitors produced sustained increases in heart rate, left ventricular contractility, and cardiac output, and decreases in mean arterial pressure. Cardiac pathology findings associated with these 2 kinase inhibitors were accompanied not only by cardiac troponin elevations but also associated with hemodynamic changes, highlighting the importance of the link of the physiologic-toxicologic interplay in cardiovascular safety assessment.

Drugging the entire human proteome: Are we there yet?

Each of the ∼20,000 proteins in the human proteome is a potential target for compounds that bind to it and modify its function. The 3D structures of most of these proteins are now available. Here, we discuss the prospects for using these structures to perform proteome-wide virtual HTS (VHTS). We compare physics-based (docking) and AI VHTS approaches, some of which are now being applied with large databases of compounds to thousands of targets. Although preliminary proteome-wide screens are now within our grasp, further methodological developments are expected to improve the accuracy of the results.

Exposure to the Natural Compound Climacostol Induces Cell Damage and Oxidative Stress in the Fruit Fly Drosophila melanogaster

The ciliate Climacostomum virens produces the metabolite climacostol that displays antimicrobial activity and cytotoxicity on human and rodent tumor cells. Given its potential as a backbone in pharmacological studies, we used the fruit fly Drosophila melanogaster to evaluate how the xenobiotic climacostol affects biological systems in vivo at the organismal level. Food administration with climacostol demonstrated its harmful role during larvae developmental stages but not pupation. The midgut of eclosed larvae showed apoptosis and increased generation of reactive oxygen species (ROS), thus demonstrating gastrointestinal toxicity. Climacostol did not affect enteroendocrine cell proliferation, suggesting moderate damage that does not initiate the repairing program. The fact that climacostol increased brain ROS and inhibited the proliferation of neural cells revealed a systemic (neurotoxic) role of this harmful substance. In this line, we found lower expression of relevant antioxidant enzymes in the larvae and impaired mitochondrial activity. Adult offsprings presented no major alterations in survival and mobility, as well the absence of abnormal phenotypes. However, mitochondrial activity and oviposition behavior was somewhat affected, indicating the chronic toxicity of climacostol, which continues moderately until adult stages. These results revealed for the first time the detrimental role of ingested climacostol in a non-target multicellular organism.

Computational analysis of potential drug-like compounds from Solanum torvum - A promising phytotherapeutics approach for the treatment of diabetes

Diabetes mellitus (DM) is a global pandemic that is characterized by high blood glucose levels. Conventional treatments have limitations, leading to the search for natural alternatives. This study focused on Solanum torvum (STV), a medicinal plant, to identify potential anti-diabetic compounds using molecular docking and molecular dynamics simulations. We focused on identifying natural inhibitors of two key enzymes involved in glucose metabolism: α-amylase (1HNY) and α-glucosidase (4J5T). In our preliminary docking study, rutin showed the highest binding affinity (-11.58 kcal/mol) to α-amylase, followed by chlorogenin (-7.58 kcal/mol) and myricetin (-5.82 kcal/mol). For α-glucosidase, rutin had the highest binding affinity (-11.78 kcal/mol), followed by chlorogenin (-7.11 kcal/mol) and fisetin (-6.44 kcal/mol). Hence, chlorogenin and rutin were selected for further analysis and compared with acarbose, an FDA-approved antidiabetic drug. Comparative docking revealed that chlorogenin had the highest binding affinity of (-9.9 kcal/mol) > rutin (-8.7 kcal/mol) and > acarbose (-7.7 kcal/mol) for α-amylase. While docking with α-glucosidase, chlorogenin again had the highest binding affinity of (-9.8 kcal/mol) > compared to rutin (-9.5 kcal/mol) and acarbose (-7.9 kcal/mol). Molecular dynamics (MD) simulations were conducted to assess their stability. We simulated 100 nanoseconds (ns) trajectories to analyze their stability on various parameters, including RMSD, RMSF, RG, SASA, H-bond analysis, PCA, FEL, and MM-PBSA on the six docked proteins. In conclusion, our study suggests that chlorogenin and rutin derived from STV may be effective natural therapeutic agents for diabetes management because of their strong binding affinities for the α-amylase and α-glucosidase enzymes.Communicated by Ramaswamy H. Sarma.

Extent of safety database in pediatric drug development: types of assessment, analytical precision, and pathway for extrapolation through on-target effects

Pediatric patients should have access to medicines that have been appropriately evaluated for safety and efficacy through revised labelling. Given this goal, the adequacy of the pediatric clinical development plan and resulting safety database are critical factors to inform a favorable benefit-risk assessment for the intended use of the medicinal product. While extrapolation from adults can be used to support efficacy of drugs in children, there may be a reluctance to use the same approach in safety assessments, wiping out potential gains in trial efficiency through a reduction of sample size. To address this issue, we explore safety review in pediatric trials, including specific types of safety assessments and precision on the estimation of event rates for specific adverse events (AEs) that can be achieved. In addition, we discuss the assessments which can provide a benchmark for the use of extrapolation of safety that focuses on on-target effects. Finally, we explore a unified approach for understanding precision using Bayesian approaches as the most appropriate methodology to describe or ascertain risk in probabilistic terms for the estimate of the event rate of specific AEs.

Off-target pharmacological activity at various kinases: Potential functional and pathological side effects

In drug discovery, during the lead optimization and candidate characterization stages, novel small molecules are frequently evaluated in a battery of in vitro pharmacology assays to identify potential unintended, off-target interactions with various receptors, transporters, ion channels, and enzymes, including kinases. Furthermore, these screening panels may also provide utility at later stages of development to provide a mechanistic understanding of unexpected safety findings. Here, we present a compendium of the most likely functional and pathological outcomes associated with interaction(s) to a panel of 95 kinases based on an extensive curation of the scientific literature. This panel of kinases was designed by AbbVie based on safety-related data extracted from the literature, as well as from over 20 years of institutional knowledge generated from discovery efforts. For each kinase, the scientific literature was reviewed using online databases and the most often reported functional and pathological effects were summarized. This work should serve as a practical guide for small molecule drug discovery scientists and clinical investigators to predict and/or interpret adverse effects related to pharmacological interactions with these kinases.

A preclinical secondary pharmacology resource illuminates target-adverse drug reaction associations of marketed drugs

In vitro secondary pharmacology assays are an important tool for predicting clinical adverse drug reactions (ADRs) of investigational drugs. We created the Secondary Pharmacology Database (SPD) by testing 1958 drugs using 200 assays to validate target-ADR associations. Compared to public and subscription resources, 95% of all and 36% of active (AC50 < 1 µM) results are unique to SPD, with bias towards higher activity in public resources. Annotating drugs with free maximal plasma concentrations, we find 684 physiologically relevant unpublished off-target activities. Furthermore, 64% of putative ADRs linked to target activity in key literature reviews are not statistically significant in SPD. Systematic analysis of all target-ADR pairs identifies several putative associations supported by publications. Finally, candidate mechanisms for known ADRs are proposed based on SPD off-target activities. Here we present a freely-available resource for benchmarking ADR predictions, explaining phenotypic activity and investigating clinical properties of marketed drugs.

ProfhEX: AI-based platform for small molecules liability profiling

Off-target drug interactions are a major reason for candidate failure in the drug discovery process. Anticipating potential drug's adverse effects in the early stages is necessary to minimize health risks to patients, animal testing, and economical costs. With the constantly increasing size of virtual screening libraries, AI-driven methods can be exploited as first-tier screening tools to provide liability estimation for drug candidates. In this work we present ProfhEX, an AI-driven suite of 46 OECD-compliant machine learning models that can profile small molecules on 7 relevant liability groups: cardiovascular, central nervous system, gastrointestinal, endocrine, renal, pulmonary and immune system toxicities. Experimental affinity data was collected from public and commercial data sources. The entire chemical space comprised 289'202 activity data for a total of 210'116 unique compounds, spanning over 46 targets with dataset sizes ranging from 819 to 18896. Gradient boosting and random forest algorithms were initially employed and ensembled for the selection of a champion model. Models were validated according to the OECD principles, including robust internal (cross validation, bootstrap, y-scrambling) and external validation. Champion models achieved an average Pearson correlation coefficient of 0.84 (SD of 0.05), an R² determination coefficient of 0.68 (SD = 0.1) and a root mean squared error of 0.69 (SD of 0.08). All liability groups showed good hit-detection power with an average enrichment factor at 5% of 13.1 (SD of 4.5) and AUC of 0.92 (SD of 0.05). Benchmarking against already existing tools demonstrated the predictive power of ProfhEX models for large-scale liability profiling. This platform will be further expanded with the inclusion of new targets and through complementary modelling approaches, such as structure and pharmacophore-based models. ProfhEX is freely accessible at the following address: https://profhex.exscalate.eu/ .

Using ChEMBL to Complement Schistosome Drug Discovery

Schistosomiasis is one of the most important neglected tropical diseases. Until an effective vaccine is registered for use, the cornerstone of schistosomiasis control remains chemotherapy with praziquantel. The sustainability of this strategy is at substantial risk due to the possibility of praziquantel insensitive/resistant schistosomes developing. Considerable time and effort could be saved in the schistosome drug discovery pipeline if available functional genomics, bioinformatics, cheminformatics and phenotypic resources are systematically leveraged. Our approach, described here, outlines how schistosome-specific resources/methodologies, coupled to the open-access drug discovery database ChEMBL, can be cooperatively used to accelerate early-stage, schistosome drug discovery efforts. Our process identified seven compounds (fimepinostat, trichostatin A, NVP-BEP800, luminespib, epoxomicin, CGP60474 and staurosporine) with ex vivo anti-schistosomula potencies in the sub-micromolar range. Three of those compounds (epoxomicin, CGP60474 and staurosporine) also demonstrated potent and fast-acting ex vivo effects on adult schistosomes and completely inhibited egg production. ChEMBL toxicity data were also leveraged to provide further support for progressing CGP60474 (as well as luminespib and TAE684) as a novel anti-schistosomal compound. As very few compounds are currently at the advanced stages of the anti-schistosomal pipeline, our approaches highlight a strategy by which new chemical matter can be identified and quickly progressed through preclinical development.

Disconnect between COX-2 selective inhibition and cardiovascular risk in preclinical models

INTRODUCTION

Secondary pharmacology profiling is routinely applied in pharmaceutical drug discovery to investigate the pharmaceutical effects of a drug at molecular targets distinct from (off-target) the intended therapeutic molecular target (on-target). Data from a randomized, placebo-controlled clinical trial, the APPROVe (Adenomatous Polyp Prevention on VIOXX, rofecoxib) trial, raised significant concerns about COX-2 inhibition as a primary or secondary target, shaping the screening and decision-making processes of some pharmaceutical companies. COX-2 is often included in off-target screens due to cardiovascular (CV) safety concerns about secondary interactions with this target. Several potential mechanisms of COX-2-mediated myocardial infarctions have been considered including, effects on platelet stickiness/aggregation, vasal tone and blood pressure, and endothelial cell activation. In the present study, we focused on each of these mechanisms as potential effects of COX-2 inhibitors, to find evidence of mechanism using various in vitro and in vivo preclinical models.

METHODS

Compounds tested in the study, with a range of COX-2 selectivity, included rofecoxib, celecoxib, etodolac, and meloxicam. Compounds were screened for inhibition of COX-2 vs COX-1 enzymatic activity, ex vivo platelet aggregation (using whole blood from multiple species), ex vivo canine femoral vascular ring model, in vitro human endothelial cell activation (with and without COX-2 induction), and in vivo cardiovascular assessment (anesthetized dog).

RESULTS

The COX-2 binding assessment generally confirmed the COX-2 selectivity previously reported. COX-2 inhibitors did not have effects on platelet function (spontaneous aggregation or inhibition of aggregation), cardiovascular parameters (mean arterial pressure, heart rate, and left ventricular contractility), or endothelial cell activation. However, rofecoxib uniquely produced an endothelial mediated constriction response in canine femoral arteries.

CONCLUSION

Our data suggest that rofecoxib-related cardiovascular events in humans are not predicted by COX-2 potency or selectivity. In addition, the vascular ring model suggested possible adverse cardiovascular effects by COX-2 inhibitors, although these effects were not seen in vivo studies. These results may also suggest that COX-2 inhibition alone is not responsible for rofecoxib-mediated adverse cardiovascular outcomes.

Using chemical and biological data to predict drug toxicity

Various sources of information can be used to better understand and predict compound activity and safety-related endpoints, including biological data such as gene expression and cell morphology. In this review, we first introduce types of chemical, in vitro and in vivo information that can be used to describe compounds and adverse effects. We then explore how compound descriptors based on chemical structure or biological perturbation response can be used to predict safety-related endpoints, and how especially biological data can help us to better understand adverse effects mechanistically. Overall, the described applications demonstrate how large-scale biological information presents new opportunities to anticipate and understand the biological effects of compounds, and how this can support predictive toxicology and drug discovery projects.

Targeting RNA with small molecules-A safety perspective

RNA is a major player in cellular function, and consequently can drive a number of disease pathologies. Over the past several years, small molecule-RNA targeting (smRNA targeting) has developed into a promising drug discovery approach. Numerous techniques, tools, and assays have been developed to support this field, and significant investments have been made by pharmaceutical and biotechnology companies. To date, the focus has been on identifying disease validated primary targets for smRNA drug development, yet RNA as a secondary (off) target for all small molecule drug programs largely has been unexplored. In this perspective, we discuss structure, target, and mechanism-driven safety aspects of smRNAs and highlight how these parameters can be evaluated in drug discovery programs to produce potentially safer drugs.

Ion Channels in Endometrial Cancer

Simple Summary

Uterine or endometrial cancer is one of the most common types of cancer among the female population. Different alterations of molecules are related to many types of cancer. Some molecules called ion channels have been described as involved in the development of cancer, including endometrial cancer. We review the scientific evidence about the involvement of the ion channels in endometrial cancer and how some treatments can be developed with these molecules as a target. Even though they are involved in the progression of endometrial cancer, since they are present throughout the whole body, some possible treatments based on these could be studied.

Abstract

Uterine or endometrial cancer (EC) is the sixth most common neoplasia among women worldwide. Cancer can originate from a myriad of causes, and increasing evidence suggests that ion channels (IC) play an important role in the process of carcinogenesis, taking part in many pathways such as self-sufficiency in growth signals, proliferation, evasion of programmed cell death (apoptosis), angiogenesis, cell differentiation, migration, adhesion, and metastasis. Hormones and growth factors are well-known to be involved in the development and/or progression of many cancers and can also regulate some ion channels and pumps. Since the endometrium is responsive and regulated by these factors, the ICs could make an important contribution to the development and progression of endometrial cancer. In this review, we explore what is beyond (ion) flow regulation by investigating the role of the main families of ICs in EC, including as possible targets for EC treatment.

Analysis of secondary pharmacology assays received by the US Food and Drug Administration

Secondary pharmacology studies are a time-efficient and cost-effective method for determining the safety profile of a potential new drug before it enters human trials. The results of these multi-target screens are commonly submitted with Investigational New Drug (IND) applications, but there currently is little guidance on how such information is presented and which targets are chosen for testing. In this study, we expand on our previous analysis of secondary pharmacology reports by manually curating and analyzing all secondary pharmacology results received by the FDA received as part of an IND submission. A total of 1120 INDs submitted by 480 sponsors between 1999 and October 2020 were included in this study. The overall results were largely consistent with previous internal and external studies, showing that the most tested target in our set was the histamine 1 receptor (tested 938 times), the most hit target was sodium channel site 2 (hit 141 times), and the target with the highest hit percentage was the vesicular monoamine transporter 2 (hit 42.2% of the time). Additionally, this study demonstrated that improvements in the secondary pharmacology submission process, such as changes in formatting and nomenclature, could enhance the utility of these assays for regulatory review, including assisting with identifying the safety liabilities of a drug candidate early in development. This updated data set will allow FDA-industry collaborative working groups to continue developing the best methods for regulatory submission of secondary pharmacology data and evaluate the need for a standard target panel.

Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches

Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.

DeepREAL: A Deep Learning Powered Multi-scale Modeling Framework for Predicting Out-of-distribution Ligand-induced GPCR Activity

Motivation

Drug discovery has witnessed intensive exploration of predictive modeling of drug–target physical interactions over two decades. However, a critical knowledge gap needs to be filled for correlating drug–target interactions with clinical outcomes: predicting genome-wide receptor activities or function selectivity, especially agonist versus antagonist, induced by novel chemicals. Two major obstacles compound the difficulty on this task: known data of receptor activity is far too scarce to train a robust model in light of genome-scale applications, and real-world applications need to deploy a model on data from various shifted distributions.

Results

To address these challenges, we have developed an end-to-end deep learning framework, DeepREAL, for multi-scale modeling of genome-wide ligand-induced receptor activities. DeepREAL utilizes self-supervised learning on tens of millions of protein sequences and pre-trained binary interaction classification to solve the data distribution shift and data scarcity problems. Extensive benchmark studies on G-protein coupled receptors (GPCRs), which simulate real-world scenarios, demonstrate that DeepREAL achieves state-of-the-art performances in out-of-distribution settings. DeepREAL can be extended to other gene families beyond GPCRs.

Availability and implementation

All data used are downloaded from Pfam (Mistry et al., 2020), GLASS (Chan et al., 2015) and IUPHAR/BPS and the data from reference (Sakamuru et al., 2021). Readers are directed to their official website for original data. Code is available on GitHub https://github.com/XieResearchGroup/DeepREAL.

Supplementary information

Supplementary data are available at Bioinformatics online.

OUP accepted manuscript

The content of this article derives from a Health and Environmental Sciences Institute (HESI) consortium with a focus to improve cardiac safety during drug development. A detailed literature review was conducted to evaluate the concordance between nonclinical repolarization assays and the clinical thorough QT (TQT) study. Food and Drug Administration and HESI developed a joint database of nonclinical and clinical data, and a retrospective analysis of 150 anonymized drug candidates was reviewed to compare the performance of 3 standard nonclinical assays with clinical TQT study findings as well as investigate mechanism(s) potentially responsible for apparent discrepancies identified. The nonclinical assays were functional (I_Kr) current block (Human ether-a-go-go related gene), action potential duration, and corrected QT interval in animals (in vivo corrected QT). Although these nonclinical assays demonstrated good specificity for predicting negative clinical QT prolongation, they had relatively poor sensitivity for predicting positive clinical QT prolongation. After review, 28 discordant TQT-positive drugs were identified. This article provides an overview of direct and indirect mechanisms responsible for QT prolongation and theoretical reasons for lack of concordance between clinical TQT studies and nonclinical assays. We examine 6 specific and discordant TQT-positive drugs as case examples. These were derived from the unique HESI/Food and Drug Administration database. We would like to emphasize some reasons for discordant data including, insufficient or inadequate nonclinical data, effects of the drug on other cardiac ion channels, and indirect and/or nonelectrophysiological effects of drugs, including altered heart rate. We also outline best practices that were developed based upon our evaluation.

[Technology and evaluation best practices for in vivo cardiovascular safety pharmacology studies]

In vivo cardiovascular experiments as part of safety pharmacology studies have been developed for small molecule drug candidates to maximize detection power for potential undesirable pharmacodynamic effects of a drug candidate on physiological functions, and have been established with appropriate expertise. Conscious freely-moving telemeterized non-rodents are generally used for the in vivo cardiovascular experiments. The technology and evaluation best practices for the experiments have been optimized by multiple researchers and as a result, the experiments considerably contribute to the estimation of cardiovascular risks for humans. In addition, as described in ICH E14&S7B Q&A draft, non-clinical studies are gaining importance in the integrated risk assessment for QT prolongation in humans, and high quality data obtained in non-clinical studies are being required. This manuscript introduces actual technology and evaluation for in vivo cardiovascular safety pharmacology studies based on Japan activity for Improvement of Cardiovascular Evaluation by Telemetry system (J-ICET), which is one of the working groups hosted by Japanese Safety Pharmacology Society.

In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity

Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a xenobiotic. For example, in pharmaceutical research and development it is one of the major reasons for drug withdrawals, clinical failures, and discontinuation of drug candidates. The development of faster and cheaper methods to assess hepatotoxicity that are both more sustainable and more informative is critically needed. The biological mechanisms and processes underpinning hepatotoxicity are summarized and experimental approaches to support the prediction of hepatotoxicity are described, including toxicokinetic considerations. The paper describes the increasingly important role of in silico approaches and highlights challenges to the adoption of these methods including the lack of a commonly agreed upon protocol for performing such an assessment and the need for in silico solutions that take dose into consideration. A proposed framework for the integration of in silico and experimental information is provided along with a case study describing how computational methods have been used to successfully respond to a regulatory question concerning non-genotoxic impurities in chemically synthesized pharmaceuticals.

In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities

The kidneys, heart and lungs are vital organ systems evaluated as part of acute or chronic toxicity assessments. New methodologies are being developed to predict these adverse effects based on in vitro and in silico approaches. This paper reviews the current state of the art in predicting these organ toxicities. It outlines the biological basis, processes and endpoints for kidney toxicity, pulmonary toxicity, respiratory irritation and sensitization as well as functional and structural cardiac toxicities. The review also covers current experimental approaches, including off-target panels from secondary pharmacology batteries. Current in silico approaches for prediction of these effects and mechanisms are described as well as obstacles to the use of in silico methods. Ultimately, a commonly accepted protocol for performing such assessment would be a valuable resource to expand the use of such approaches across different regulatory and industrial applications. However, a number of factors impede their widespread deployment including a lack of a comprehensive mechanistic understanding, limited in vitro testing approaches and limited in vivo databases suitable for modeling, a limited understanding of how to incorporate absorption, distribution, metabolism, and excretion (ADME) considerations into the overall process, a lack of in silico models designed to predict a safe dose and an accepted framework for organizing the key characteristics of these organ toxicants.

Paving the way for application of next generation risk assessment to safety decision-making for cosmetic ingredients

Next generation risk assessment (NGRA) is an exposure-led, hypothesis-driven approach that has the potential to support animal-free safety decision-making. However, significant effort is needed to develop and test the in vitro and in silico (computational) approaches that underpin NGRA to enable confident application in a regulatory context. A workshop was held in Montreal in 2019 to discuss where effort needs to be focussed and to agree on the steps needed to ensure safety decisions made on cosmetic ingredients are robust and protective. Workshop participants explored whether NGRA for cosmetic ingredients can be protective of human health, and reviewed examples of NGRA for cosmetic ingredients. From the limited examples available, it is clear that NGRA is still in its infancy, and further case studies are needed to determine whether safety decisions are sufficiently protective and not overly conservative. Seven areas were identified to help progress application of NGRA, including further investments in case studies that elaborate on scenarios frequently encountered by industry and regulators, including those where a ‘high risk’ conclusion would be expected. These will provide confidence that the tools and approaches can reliably discern differing levels of risk. Furthermore, frameworks to guide performance and reporting should be developed.

Aggregation and analysis of secondary pharmacology data from investigational new drug submissions at the US Food and Drug Administration

Secondary pharmacology studies are utilized by the pharmaceutical industry as a cost-efficient tool to identify potential safety liabilities of drugs before entering Phase 1 clinical trials. These studies are recommended by the Food and Drug Administration (FDA) as a part of the Investigational New Drug (IND) application. However, despite the utility of these assays, there is little guidance on which targets should be screened and which format should be used. Here, we evaluated 226 secondary pharmacology profiles obtained from close to 90 unique sponsors. The results indicated that the most tested target in our set was the GABA benzodiazepine receptor (tested 168 times), the most hit target was adenosine 3 (hit 24 times), and the target with the highest hit percentage was the quinone reductase 2 (NQO2) receptor (hit 29% of the time). The overall results were largely consistent with those observed in previous publications. However, this study also identified the need for improvement in the submission process of secondary pharmacology studies by industry, which could enhance their utility for regulatory purpose. FDA-industry collaborative working groups will utilize this data to determine the best methods for regulatory submission of these studies and evaluate the need for a standard target panel.

Large scale meta-analysis of preclinical toxicity data for target characterisation and hypotheses generation

Recent technological advances in the field of big data have increased our capabilities to query large databases and combine information from different domains and disciplines. In the area of preclinical studies, initiatives like SEND (Standard for Exchange of Nonclinical Data) will also contribute to collect and present nonclinical data in a consistent manner and increase analytical possibilities. With facilitated access to preclinical data and improvements in analytical algorithms there will surely be an expectation for organisations to ensure all the historical data available to them is leveraged to build new hypotheses. These kinds of analyses may soon become as important as the animal studies themselves, in addition to being critical components to achieve objectives aligned with 3Rs. This article proposes the application of meta-analyses at large scale in corporate databases as a tool to exploit data from both preclinical studies and in vitro pharmacological activity assays to identify associations between targets and tissues that can be used as seeds for the development of causal hypotheses to characterise of targets. A total of 833 in-house preclinical toxicity studies relating to 416 compounds reported to be active (pXC50 ≥ 5.5) against a panel of 96 selected targets of interest for potential off-target non desired effects were meta-analysed, aggregating the data in tissue-target pairs. The primary outcome was the odds ratio (OR) of the number of animals with observed events (any morphology, any severity) in treated and control groups in the tissue analysed. This led to a total of 2139 meta-analyses producing a total of 364 statistically significant associations (random effects model), 121 after adjusting by multiple comparison bias. The results show the utility of the proposed approach to leverage historical corporate data and may offer a vehicle for researchers to share, aggregate and analyse their preclinical toxicological data in precompetitive environments.

NMS-873 functions as a dual inhibitor of mitochondrial oxidative phosphorylation

Small-molecule inhibitors of enzyme function are critical tools for the study of cell biological processes and for treatment of human disease. Identifying inhibitors with suitable specificity and selectivity for single enzymes, however, remains a challenge. In this study we describe our serendipitous discovery that NMS-873, a compound that was previously identified as a highly selective allosteric inhibitor of the ATPase valosin-containing protein (VCP/p97), rapidly induces aerobic fermentation in cultured human and mouse cells. Our further investigation uncovered an unexpected off-target effect of NMS-873 on mitochondrial oxidative phosphorylation, specifically as a dual inhibitor of Complex I and ATP synthase. This work points to the need for caution regarding the interpretation of cell survival data associated with NMS-873 treatment and indicates that cellular toxicity associated with its use may be caused by both VCP/p97-dependent and VCP/p97-independent mechanisms.

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.

Antitubercular 2-Pyrazolylpyrimidinones: Structure-Activity Relationship and Mode-of-Action Studies

Phenotypic screening of a Medicines for Malaria Venture compound library against Mycobacterium tuberculosis (Mtb) identified a cluster of pan-active 2-pyrazolylpyrimidinones. The biology triage of these actives using various tool strains of Mtb suggested a novel mechanism of action. The compounds were bactericidal against replicating Mtb and retained potency against clinical isolates of Mtb. Although selected MmpL3 mutant strains of Mtb showed resistance to these compounds, there was no shift in the minimum inhibitory concentration (MIC) against a mmpL3 hypomorph, suggesting mutations in MmpL3 as a possible resistance mechanism for the compounds but not necessarily as the target. RNA transcriptional profiling and the checkerboard board 2D-MIC assay in the presence of varying concentrations of ferrous salt indicated perturbation of the Fe-homeostasis by the compounds. Structure-activity relationship studies identified potent compounds with good physicochemical properties and in vitro microsomal metabolic stability with moderate selectivity over cytotoxicity against mammalian cell lines.

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.

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.

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.

Direct Delivery of Apatite Nanoparticle-Encapsulated siRNA Targeting TIMP-1 for Intractable Abnormal Scars

Hypertrophic scars (HSs) and keloids are histologically characterized by excessive extracellular matrix (ECM) deposition. ECM deposition depends on the balance between matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteases (TIMPs). TIMP-1 has been linked to ECM degradation and is therefore a promising therapeutic strategy. In this study, we generated super carbonate apatite (sCA) nanoparticle-encapsulated TIMP-1 small interfering RNA (siRNA) (siTIMP1) preparations and examined the effect of local injections on mouse HSs and on ex vivo-cultured keloids. The sCA-siTIMP1 injections significantly reduced scar formation, scar cross-sectional areas, collagen densities, and collagen types I and III levels in the lesions. None of the mice died or exhibited abnormal endpoints. Apatite accumulation was not detected in the other organs. In an ex vivo keloid tissue culture system, sCA-siTIMP1 injections reduced the thickness and complexity of collagen bundles. Our results showed that topical sCA-siTIMP1 injections during mechanical stress-induced HS development reduced scar size. When keloids were injected three times with sCA-siTIMP1 during 6 days, keloidal collagen levels decreased substantially. Accordingly, sCA-siRNA delivery may be an effective approach for keloid treatment, and further investigations are needed to enable its practical use.

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.

Utilizing drug-target-event relationships to unveil safety patterns in pharmacovigilance

INTRODUCTION

Signal detection is the most pivotal activity of signal management to guarantee that drugs maintain a positive risk-benefit ratio during their lifetime on the market. Signal detection is based on the systematic evaluation of available data sources, which have recently been extended in order to improve timely and comprehensive signal detection of drug safety problems.

AREAS COVERED

In recent years, attempts have been made to incorporate pharmacological data for the prediction of safety signals. Previous studies have shown that data on the pharmacological targets of drugs are predictive of post-marketing adverse events. However, current approaches limit such predictions to adverse events expected from the interaction of a drug with the main pharmacological target and do not take off-target interactions into consideration.

EXPERT OPINION

The authors propose the application of predictive modeling techniques utilizing pharmacological data from public databases for predicting drug-target-event relationships deriving from main- and off-target binding and from which potential safety signals can be deduced. Additionally, they provide an operative procedure for the identification of clinically relevant subgroups for predicted safety signals.

A practical guide to secondary pharmacology in drug discovery

Secondary pharmacological profiling is increasingly applied in pharmaceutical drug discovery to address unwanted pharmacological side effects of drug candidates before entering the clinic. Regulators, drug makers and patients share a demand for deep characterization of secondary pharmacology effects of novel drugs and their metabolites. The scope of such profiling has therefore expanded substantially in the past two decades, leading to the implementation of broad in silico profiling methods and focused in vitro off-target screening panels, to identify liabilities, but also opportunities, as early as possible. The pharmaceutical industry applies such panels at all stages of drug discovery routinely up to early development. Nevertheless, target composition, screening technologies, assay formats, interpretation and scheduling of panels can vary significantly between companies in the absence of dedicated guidelines. To contribute towards best practices in secondary pharmacology profiling, this review aims to summarize the state-of-the art in this field. Considerations are discussed with respect to panel design, screening strategy, implementation and interpretation of the data, including regulatory perspectives. The cascaded, or integrated, use of in silico and off-target profiling allows to exploit synergies for comprehensive safety assessment of drug candidates.

Today's Challenges to De-Risk and Predict Drug Safety in Human "Mind-the-Gap"

Current gaps in drug safety sciences can result from the inability (1) to identify hazard across multiple target organs, (2) to predict and risk assess with certainty against drug safety liabilities for the major target organs, (3) to optimally manage and mitigate against drug safety liabilities, and (4) to apply principles of governance on the generation, integration, and use of experimental data. Translational safety assessment to evaluate several target-organ drug toxicities can only be partially achieved by use of current in vitro and in vivo test systems. What remains to be tackled necessitates the deployment of in vitro-human-relevant test systems to address human specific or selective forms of toxicities. Nevertheless, such models may only address in part some of the requirements in today's armament of biomedical tools essential for improving the discovery of drug candidates. Refinement of in silico tools, Target Safety Assessment and a greater understanding of mechanistic insights of toxicities might provide future opportunities to better identify drug safety liabilities. The increasing diversity of drug modalities present further challenges for nonclinical and clinical development requiring further research to develop suitable test systems and technologies. Our ability to optimally manage and mitigate safety risk will come from the greater refinement of safety margin estimates, provision and use of human-relevant safety biomarkers, and understanding of the translation from in silico, in vitro, and in vivo studies to human. An improvement of governance frameworks and standards at all levels within organizations, national, and international, can only help facilitate drug discovery and development programs.

Clinical Implications and Translation of an Off-Target Pharmacology Profiling Hit: Adenosine Uptake Inhibition In Vitro

Off-target activities of drug candidates observed during in vitro pharmacological profiling frequently do not translate to adverse events (AEs) in human. This could be because off-target activities do not have functional consequences, are not observed at exposures achieved during clinical testing, or may not translate into clinical outcomes. We report clinical consequences of an off-target activity observed during profiling of AMG 337, a selective inhibitor of the mesenchymal-epithelial transition factor being evaluated for treatment of solid tumors. In our screen of 151 potential off-targets, AMG 337 inhibited only adenosine transporter (AT). During clinical trials, headache emerged as the dose-limiting AE in the first-in-human trial. It was thought that headache was caused by extracellular accumulation of adenosine from inhibition of AT by AMG 337 and subsequent adenosine-mediated vasodilation through adenosine receptors (ARs). Further nonclinical studies were performed to evaluate this hypothesis. AMG 337 inhibited AT function in dog and human cells in vitro and dog and human arteries ex vivo. In a dog telemetry study, AMG 337 caused hypotension, which was reduced by pretreatment with theophylline, an AR antagonist. Overall, nonclinical and clinical data suggested that headache was due to cerebral vasorelaxation caused by AMG 337-mediated inhibition of AT. When subjects were advised to drink coffee, an AR antagonist, prior to AMG 337, the severity of headaches was reduced, allowing them to continue treatment. These findings demonstrate the importance of carefully evaluating clinical observations during early drug development and the value of translational nonclinical studies to investigate the mechanism of action driving clinical observations.

Novel Computational Approach to Predict Off-Target Interactions for Small Molecules

Most small molecule drugs interact with unintended, often unknown, biological targets and these off-target interactions may lead to both preclinical and clinical toxic events. Undesired off-target interactions are often not detected using current drug discovery assays, such as experimental polypharmacological screens. Thus, improvement in the early identification of off-target interactions represents an opportunity to reduce safety-related attrition rates during preclinical and clinical development. In order to better identify potential off-target interactions that could be linked to predictable safety issues, a novel computational approach to predict safety-relevant interactions currently not covered was designed and evaluated. These analyses, termed Off-Target Safety Assessment (OTSA), cover more than 7,000 targets (~35% of the proteome) and > 2,46,704 preclinical and clinical alerts (as of January 20, 2019). The approach described herein exploits a highly curated training set of >1 million compounds (tracking >20 million compound-structure activity relationship/SAR data points) with known in vitro activities derived from patents, journals, and publicly available databases. This computational process was used to predict both the primary and secondary pharmacological activities for a selection of 857 diverse small molecule drugs for which extensive secondary pharmacology data are readily available (456 discontinued and 401 FDA approved). The OTSA process predicted a total of 7,990 interactions for these 857 molecules. Of these, 3,923 and 4,067 possible high-scoring interactions were predicted for the discontinued and approved drugs, respectively, translating to an average of 9.3 interactions per drug. The OTSA process correctly identified the known pharmacological targets for >70% of these drugs, but also predicted a significant number of off-targets that may provide additional insight into observed in vivo effects. About 51.5% (2,025) and 22% (900) of these predicted high-scoring interactions have not previously been reported for the discontinued and approved drugs, respectively, and these may have a potential for repurposing efforts. Moreover, for both drug categories, higher promiscuity was observed for compounds with a MW range of 300 to 500, TPSA of ~200, and clogP ≥7. This computation also revealed significantly lower promiscuity (i.e., number of confirmed off-targets) for compounds with MW > 700 and MW<200 for both categories. In addition, 15 internal small molecules with known off-target interactions were evaluated. For these compounds, the OTSA framework not only captured about 56.8% of in vitro confirmed off-target interactions, but also identified the right pharmacological targets for 14 compounds as one of the top scoring targets. In conclusion, the OTSA process demonstrates good predictive performance characteristics and represents an additional tool with utility during the lead optimization stage of the drug discovery process. Additionally, the computed physiochemical properties such as clogP (i.e., lipophilicity), molecular weight, pKa and logS (i.e., solubility) were found to be statistically different between the approved and discontinued drugs, but the internal compounds were close to the approved drugs space in most part.

Evaluation of the serum metabolome of patients with alkaptonuria before and after two years of treatment with nitisinone using LC-QTOF-MS

BACKGROUND

The homogentisic acid-lowering therapy nitisinone is being evaluated for the treatment of alkaptonuria (AKU) at the National Centre for AKU. Beyond hypertyrosinemia, the wider metabolic consequences of its use are largely unknown. The aim of this work was to evaluate the impact of nitisinone on the serum metabolome of patients with AKU after 12 and 24 months of treatment.

METHODS

Deproteinized serum from 25 patients with AKU (mean age[±SD] 51.1 ± 14.9 years, 12 male) was analyzed using the 1290 Infinity II liquid chromatography system coupled to a 6550 quadrupole time-of-flight mass spectrometry (Agilent, UK). Raw data were processed using a batch targeted feature extraction algorithm and an accurate mass retention time database containing 469 intermediary metabolites (MW 72-785). Matched entities (±10 ppm theoretical accurate mass and ±0.3 minutes retention time window) were filtered based on their frequency and variability (<25% CV) in group quality control samples, and repeated measures statistical significance analysis with Benjamini-Hochberg false discovery rate adjustment was used to assess changes in metabolite abundance.

RESULTS

Eight metabolites increased in abundance (log2 fold change [FC] 2.1-15.2, P < .05); 7 of 8 entities were related to tyrosine metabolism, and 13 decreased in abundance (log2 FC 1.5-15.5, P < .05); including entities related to tyrosine (n = 2), tryptophan (n = 3), xanthine (n = 2), and citric acid cycle metabolism (n = 2).

CONCLUSIONS

Evaluation of the serum metabolome of patients with AKU showed a significant difference in the abundance of several metabolites following treatment with nitisinone, including a number that have not been previously reported; several of these were not related to the tyrosine metabolic pathway.

SYNOPSIS

Nitisinone therapy has a significant impact on several metabolites beyond the tyrosine metabolic pathway, several of which appear to be related to the redox state of the cell.

Monoclonal Antibodies Targeting Ion Channels and Their Therapeutic Potential

Monoclonal antibodies (mAbs) represent a rapidly growing pharmaceutical class of protein drugs that becomes an important part of the precision therapy. mAbs are characterized by their high specificity and affinity for the target antigen, which is mostly present on the cell surface. Ion channels are a large family of transmembrane proteins that control ion transport across the cell membrane. They are involved in almost all biological processes in both health and disease and are widely considered as prospective targets. However, no antibody-based drug targeting ion channel has been developed so far that has progressed to clinical use. Thus, we provide a comprehensive review of the elaborated mAbs against ion channels, describe their mechanisms of action, and discuss their therapeutic potential.

Safety differentiation: emerging competitive edge in drug development

With increasing expectations to provide evidence of drug efficacy, safety, and cost-effectiveness, best-in-class drugs are a major value driver for the pharmaceutical industry. Superior safety is a key differentiation criterion that could be achieved through better risk:benefit profiles, safety margins, fewer contraindications, and improved patient compliance. To accomplish this, comparative safety assessments using innovative and adaptive nonclinical and clinical outcome-based approaches should be undertaken, and continuous strategic adjustments must be made as the risk:benefit profiles evolve. Key success criteria include scientific expertise and integration between all disciplines during the full extent of the drug development process.

Screening Strategies and Methods for Better Off-Target Liability Prediction and Identification of Small-Molecule Pharmaceuticals

Pharmaceutical discovery and development is a long and expensive process that, unfortunately, still results in a low success rate, with drug safety continuing to be a major impedance. Improved safety screening strategies and methods are needed to more effectively fill this critical gap. Recent advances in informatics are now making it possible to manage bigger data sets and integrate multiple sources of screening data in a manner that can potentially improve the selection of higher-quality drug candidates. Integrated screening paradigms have become the norm in Pharma, both in discovery screening and in the identification of off-target toxicity mechanisms during later-stage development. Furthermore, advances in computational methods are making in silico screens more relevant and suggest that they may represent a feasible option for augmenting the current screening paradigm. This paper outlines several fundamental methods of the current drug screening processes across Pharma and emerging techniques/technologies that promise to improve molecule selection. In addition, the authors discuss integrated screening strategies and provide examples of advanced screening paradigms.

In vitro secondary pharmacological profiling: An IQ-DruSafe industry survey on current practices

INTRODUCTION

In 2015, IQ DruSafe conducted a survey of its membership to identify industry practices related to in vitro off target pharmacological profiling of small molecules.

METHODS

An anonymous survey of 20 questions was submitted to IQ-DruSafe representatives. Questions were designed to explore screening strategies, methods employed and experience of regulatory interactions related to in vitro secondary pharmacology profiling.

RESULTS

The pharmaceutical industry routinely utilizes panels of in vitro assays to detect undesirable off-target interactions of new chemical entities that are deployed at all stages of drug discovery and early development. The formats, approaches and size of panels vary between companies, in particular i) choice of assay technology; ii) test concentration (single vs. multiple concentrations) iii) rationale for targets and panels selection (taking into account organizational experience, primary target, therapeutic area, availability at service providers) iv) threshold level for significant interaction with a target and v) data interpretation. Data are generated during the early phases of drug discovery, principally before in vivo GLP studies (i.e., hit-to-lead, lead optimization, development candidate selection) and used to contextualize in vivo non-clinical and clinical findings. Data were included in regulatory documents, and around half of respondents experienced regulatory questions about the significance of the results.

CONCLUSION

While it seems that in vitro secondary pharmacological profiling is generally considered valuable across the industry, particularly as a tool in early phases of drug discovery for small molecules, there is only loose consensus on testing paradigm, the required interpretation and suitable follow up strategies to fully understand potential risk.

Ion Channels in Drug Discovery and Safety Pharmacology

Ion channels are membrane proteins involved in almost all physiological processes, including neurotransmission, muscle contraction, pace-making activity, secretion, electrolyte and water balance, immune response, and cell proliferation. Due to their broad distribution in human body and physiological roles, ion channels are attractive targets for drug discovery and safety pharmacology. Over the years ion channels have been associated to many genetic diseases ("channelopathies"). For most of these diseases the therapy is mainly empirical and symptomatic, often limited by lack of efficacy and tolerability for a number of patients. The search for the development of new and more specific therapeutic approaches is therefore strongly pursued. At the same time acquired channelopathies or dangerous side effects (such as proarrhythmic risk) can develop as a consequence of drugs unexpectedly targeting ion channels. Several noncardiovascular drugs are known to block cardiac ion channels, leading to potentially fatal delayed ventricular repolarization. Thus, the search of reliable preclinical cardiac safety testing in early stage of drug discovery is mandatory. To fulfill these needs, both ion channels drug discovery and toxicology strategies are evolving toward comprehensive research approaches integrating ad hoc designed in silico predictions and experimental studies for a more reliable and quick translation of results to the clinic side.Here we discuss two examples of how the combination of in silico methods and patch clamp experiments can help addressing drug discovery and safety issues regarding ion channels.

Structural and Functional View of Polypharmacology

Protein domains mediate drug-protein interactions and this principle can guide the design of multi-target drugs i.e. polypharmacology. In this study, we associate multi-target drugs with CATH functional families through the overrepresentation of targets of those drugs in CATH functional families. Thus, we identify CATH functional families that are currently enriched in drugs (druggable CATH functional families) and we use the network properties of these druggable protein families to analyse their association with drug side effects. Analysis of selected druggable CATH functional families, enriched in drug targets, show that relatives exhibit highly conserved drug binding sites. Furthermore, relatives within druggable CATH functional families occupy central positions in a human protein functional network, cluster together forming network neighbourhoods and are less likely to be within proteins associated with drug side effects. Our results demonstrate that CATH functional families can be used to identify drug-target interactions, opening a new research direction in target identification.