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

A novel heuristic of rigid docking scores positively correlates with full-length nuclear receptor LRH-1 regulation

The nuclear receptor Liver Receptor Homolog-1 (LRH-1, NR5A2) is a ligand-regulated transcription factor and validated drug target for several human diseases. LRH-1 activation is regulated by small molecule ligands, which bind to the ligand binding domain (LBD) within the full-length LRH-1. We recently identified 57 compounds that bind LRH-1, and unexpectedly found these compounds regulated either the isolated LBD, or the full-length LRH-1 in cells, with little overlap. Here, we correlated compound binding energy from a single rigid-body scoring function with full-length LRH-1 activity in cells. Although docking scores of the 57 hit compounds did not correlate with LRH-1 regulation in wet lab assays, a subset of the compounds had large differences in binding energy docked to the isolated LBD vs. full-length LRH-1, which we used to empirically derive a new metric of the docking scores we call "ΔΔG". Initial regressions, correlations and contingency analyses all suggest compounds with high ΔΔG values more frequently regulated LRH-1 in wet lab assays. We then docked all 57 compounds to 18 separate crystal structures of LRH-1 to obtain averaged ΔΔG values for each compound, which robustly and reproducibly associated with full-length LRH-1 activity in cells. Network analyses on the 18 crystal structures of LRH-1 suggest unique communication paths exist between the subsets of LRH-1 crystal structures that produced high vs. low ΔΔG values, identifying a structural relationship between ΔΔG and the position of Helix 6, a previously established regulatory helix important for LRH-1 regulation. Together, these data suggest rigid-body computational docking can be used to quickly calculate ΔΔG, which positively correlated with the ability of these 57 hit compounds to regulate full-length LRH-1 in cell-based assays. We propose ΔΔG as a novel computational tool that can be applied to LRH-1 drug screens to prioritize compounds for resource-intense secondary screening.

Toxicology profile of a novel GLP1 receptor biased agonist-SAL0112 in nonhuman primates

Oral small-molecule GLP-1 receptor biased agonists exhibit promising treatment efficacy of type 2 diabetes and obesity. SAL0112 is a novel compound that has demonstrated remarkable efficacy in preclinical animal models. Herein, both in vitro and in vivo preclinical toxicity investigations were conducted to explore the safety profile of SAL0112. The HTRF assay and TR-FRET assay were utilized for cAMP detection. Patch clamp assay was employed for hERG potassium ion channel determination. Cynomolgus monkeys were used in a cardiovascular safety pharmacology study and a 13-week repeated dose toxicity study. The telemetry system was employed to detect cardiovascular indicators such as ECG, HR, and BP. During the repeated dose toxicity study, body weight, food intake, hematology, coagulation function test, serum biochemistry tests, and urine analysis were measured. Macroscopic and microscopic observations were conducted at the end of the study. TK studies were conducted on Day 1 and Day 91. SAL0112 exhibited a high degree of potency in activating the monkey GLP1 receptor whereas had no effect on the rodent GLP1 receptor. In contrast to Danuglipron, which demonstrated high potency on hERG with an IC50 value of 6.9 μM, the IC50 of SAL0112 on hERG was greater than 100 μM. Compared to the Vehicle Control group, no significant changes in cardiovascular indicators were observed in the cardiovascular safety pharmacology study after a single dose of SAL0112 up to 250 mg/kg (P > 0.05). A repeated dose toxicity study revealed moderate anorexigenic effects and a reduction in body weight, effects that were found to be reversible and not associated with any pathological changes. The NOAEL of SAL0112 is 150 mg/kg, providing an approximate safety margin of threefold. SAL0112 demonstrated a favorable safety profile in cynomolgus monkeys, with a substantial therapeutic window that supports the progression of this compound into clinical studies.

Exploring the Antiangiogenic and Anti-Inflammatory Potential of Homoisoflavonoids: Target Identification Using Biotin Probes

Chemical proteomics using biotin probes of natural products have significantly advanced our understanding of molecular targets and therapeutic potential. This review highlights recent progress in the application of biotin probes of homoisoflavonoids for identifying binding proteins and elucidating mechanisms of action. Notably, homoisoflavonoids exhibit antiangiogenic, anti-inflammatory, and antidiabetic effects. A combination of biotin probes, pull-down assays, mass spectrometry, and molecular modeling has revealed how natural products and their derivatives interact with several proteins such as ferrochelatase (FECH), soluble epoxide hydrolase (sEH), inosine monophosphate dehydrogenase 2 (IMPDH2), phosphodiesterase 4 (PDE4), and deoxyhypusine hydroxylase (DOHH). These target identification approaches pave the way for new therapeutic avenues, especially in the fields of oncology and ophthalmology. Future research aimed at expanding the repertoire of target identification using biotin probes of homoisoflavonoids promises to further elucidate the complex mechanisms and develop new drug candidates.

Exploiting Cell-Based Assays to Accelerate Drug Development for G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are relevant targets for health and disease as they regulate various aspects of metabolism, proliferation, differentiation, and immune pathways. They are implicated in several disease areas, including cancer, diabetes, cardiovascular diseases, and mental disorders. It is worth noting that about a third of all marketed drugs target GPCRs, making them prime pharmacological targets for drug discovery. Numerous functional assays have been developed to assess GPCR activity and GPCR signaling in living cells. Here, we review the current literature of genetically encoded cell-based assays to measure GPCR activation and downstream signaling at different hierarchical levels of signaling, from the receptor to transcription, via transducers, effectors, and second messengers. Singleplex assay formats provide one data point per experimental condition. Typical examples are bioluminescence resonance energy transfer (BRET) assays and protease cleavage assays (e.g., Tango or split TEV). By contrast, multiplex assay formats allow for the parallel measurement of multiple receptors and pathways and typically use molecular barcodes as transcriptional reporters in barcoded assays. This enables the efficient identification of desired on-target and on-pathway effects as well as detrimental off-target and off-pathway effects. Multiplex assays are anticipated to accelerate drug discovery for GPCRs as they provide a comprehensive and broad identification of compound effects.

The Current Status and Use of Microphysiological Systems by the Pharmaceutical Industry: The International Consortium for Innovation and Quality Microphysiological Systems Affiliate Survey and Commentary

Microphysiological systems (MPS) are comprised of one or multiple cell types of human or animal origins that mimic the biochemical/electrical/mechanical responses and blood-tissue barrier properties of the cells observed within a complex organ. The goal of incorporating these in vitro systems is to expedite and advance the drug discovery and development paradigm with improved predictive and translational capabilities. Considering the industry need for improved efficiency and the broad challenges of model qualification and acceptance, the International Consortium for Innovation and Quality (IQ) founded an IQ MPS working group in 2014 and Affiliate in 2018. This group connects thought leaders and end users, provides a forum for crosspharma collaboration, and engages with regulators to qualify translationally relevant MPS models. To understand how pharmaceutical companies are using MPS, the IQ MPS Affiliate conducted two surveys in 2019, survey 1, and 2021, survey 2, which differed slightly in the scope of definition of the complex in vitro models under question. The surveys captured demographics, resourcing, rank order for organs of interest, compound modalities tested, and MPS organ-specific questions, including nonclinical species needs and cell types. The major focus of this manuscript is on results from survey 2, where we specifically highlight the context of use for MPS within safety, pharmacology, or absorption, disposition, metabolism, and excretion and discuss considerations for including MPS data in regulatory submissions. In summary, these data provide valuable insights for developers, regulators, and pharma, offering a view into current industry practices and future considerations while highlighting key challenges impacting MPS adoption. SIGNIFICANCE STATEMENT: The application of microphysiological systems (MPS) represents a growing area of interest in the drug discovery and development framework. This study surveyed 20+ pharma companies to understand resourcing, current areas of application, and the key challenges and barriers to internal MPS adoption. These results will provide regulators, tech providers, and pharma industry leaders a starting point to assess the current state of MPS applications along with key learnings to effectively realize the potential of MPS as an emerging technology.

Chemo-proteomics in antimalarial target identification and engagement

Humans have lived in tenuous battle with malaria over millennia. Today, while much of the world is free of the disease, areas of South America, Asia, and Africa still wage this war with substantial impacts on their social and economic development. The threat of widespread resistance to all currently available antimalarial therapies continues to raise concern. Therefore, it is imperative that novel antimalarial chemotypes be developed to populate the pipeline going forward. Phenotypic screening has been responsible for the majority of the new chemotypes emerging in the past few decades. However, this can result in limited information on the molecular target of these compounds which may serve as an unknown variable complicating their progression into clinical development. Target identification and validation is a process that incorporates techniques from a range of different disciplines. Chemical biology and more specifically chemo-proteomics have been heavily utilized for this purpose. This review provides an in-depth summary of the application of chemo-proteomics in antimalarial development. Here we focus particularly on the methodology, practicalities, merits, and limitations of designing these experiments. Together this provides learnings on the future use of chemo-proteomics in antimalarial development.

Artificial Intelligence/Machine Learning-Driven Small Molecule Repurposing via Off-Target Prediction and Transcriptomics

The process of discovering small molecule drugs involves screening numerous compounds and optimizing the most promising ones, both in vitro and in vivo. However, approximately 90% of these optimized candidates fail during trials due to unexpected toxicity or insufficient efficacy. Current concepts with respect to drug-protein interactions suggest that each small molecule interacts with an average of 6-11 targets. This implies that approved drugs and even discontinued compounds could be repurposed by leveraging their interactions with unintended targets. Therefore, we developed a computational repurposing framework for small molecules, which combines artificial intelligence/machine learning (AI/ML)-based and chemical similarity-based target prediction methods with cross-species transcriptomics information. This repurposing methodology incorporates eight distinct target prediction methods, including three machine learning methods. By using multiple orthogonal methods for a "dataset" composed of 2766 FDA-approved drugs targeting multiple therapeutic target classes, we identified 27,371 off-target interactions involving 2013 protein targets (i.e., an average of around 10 interactions per drug). Relative to the drugs in the dataset, we identified 150,620 structurally similar compounds. The highest number of predicted interactions were for drugs targeting G protein-coupled receptors (GPCRs), enzymes, and kinases with 10,648, 4081, and 3678 interactions, respectively. Notably, 17,283 (63%) of the off-target interactions have been confirmed in vitro. Approximately 4000 interactions had an IC50 of <100 nM for 1105 FDA-approved drugs and 1661 interactions had an IC50 of <10 nM for 696 FDA-approved drugs. Together, the confirmation of numerous predicted interactions and the exploration of tissue-specific expression patterns in human and animal tissues offer insights into potential drug repurposing for new therapeutic applications.

A Normalization Protocol Reduces Edge Effect in High-Throughput Analyses of Hydroxyurea Hypersensitivity in Fission Yeast

Edge effect denotes better growth of microbial organisms situated at the edge of the solid agar media. Although the precise reason underlying edge effect is unresolved, it is generally attributed to greater nutrient availability with less competing neighbors at the edge. Nonetheless, edge effect constitutes an unavoidable confounding factor that results in misinterpretation of cell fitness, especially in high-throughput screening experiments widely employed for genome-wide investigation using microbial gene knockout or mutant libraries. Here, we visualize edge effect in high-throughput high-density pinning arrays and report a normalization approach based on colony growth rate to quantify drug (hydroxyurea)-hypersensitivity in fission yeast strains. This normalization procedure improved the accuracy of fitness measurement by compensating cell growth rate discrepancy at different locations on the plate and reducing false-positive and -negative frequencies. Our work thus provides a simple and coding-free solution for a struggling problem in robotics-based high-throughput screening experiments.

Discovery and In Vitro Characterization of BAY 2686013, an Allosteric Small Molecule Antagonist of the Human Pituitary Adenylate Cyclase-Activating Polypeptide Receptor

The human pituitary adenylate cyclase-activating polypeptide receptor (hPAC1-R), a class B G-protein-coupled receptor (GPCR) identified almost 30 years ago, represents an important pharmacological target in the areas of neuroscience, oncology, and immunology. Despite interest in this target, only a very limited number of small molecule modulators have been reported for this receptor. We herein describe the results of a drug discovery program aiming for the identification of a potent and selective hPAC1-R antagonist. An initial high-throughput screening (HTS) screen of 3.05 million compounds originating from the Bayer screening library failed to identify any tractable hits. A second, completely revised screen using native human embryonic kidney (HEK)293 cells yielded a small number of hits exhibiting antagonistic properties (4.2 million compounds screened). BAY 2686013 (1) emerged as a promising compound showing selective antagonistic activity in the submicromolar potency range. In-depth characterization supported the hypothesis that BAY 2686013 blocks receptor activity in a noncompetitive manner. Preclinical, pharmacokinetic profiling indicates that BAY 2686013 is a valuable tool compound for better understanding the signaling and function of hPAC1-R. SIGNIFICANCE STATEMENT: Although the human pituitary adenylate cyclase-activating polypeptide receptor (hPAC1-R) is of major significance as a therapeutic target with a well documented role in pain signaling, only a very limited number of small-molecule (SMOL) compounds are known to modulate its activity. We identified and thoroughly characterized a novel, potent, and selective SMOL antagonist of hPAC1-R (acting in an allosteric manner). These characteristics make BAY 2686013 an ideal tool for further studies.

AI/ML Models to Predict the Severity of Drug-Induced Liver Injury for Small Molecules

Drug-induced liver injury (DILI), believed to be a multifactorial toxicity, has been a leading cause of attrition of small molecules during discovery, clinical development, and postmarketing. Identification of DILI risk early reduces the costs and cycle times associated with drug development. In recent years, several groups have reported predictive models that use physicochemical properties or in vitro and in vivo assay endpoints; however, these approaches have not accounted for liver-expressed proteins and drug molecules. To address this gap, we have developed an integrated artificial intelligence/machine learning (AI/ML) model to predict DILI severity for small molecules using a combination of physicochemical properties and off-target interactions predicted in silico. We compiled a data set of 603 diverse compounds from public databases. Among them, 164 were categorized as Most DILI (M-DILI), 245 as Less DILI (L-DILI), and 194 as No DILI (N-DILI) by the FDA. Six machine learning methods were used to create a consensus model for predicting the DILI potential. These methods include k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), Naïve Bayes (NB), artificial neural network (ANN), logistic regression (LR), weighted average ensemble learning (WA) and penalized logistic regression (PLR). Among the analyzed ML methods, SVM, RF, LR, WA, and PLR identified M-DILI and N-DILI compounds, achieving a receiver operating characteristic area under the curve of 0.88, sensitivity of 0.73, and specificity of 0.9. Approximately 43 off-targets, along with physicochemical properties (fsp³, log S, basicity, reactive functional groups, and predicted metabolites), were identified as significant factors in distinguishing between M-DILI and N-DILI compounds. The key off-targets that we identified include: PTGS1, PTGS2, SLC22A12, PPARγ, RXRA, CYP2C9, AKR1C3, MGLL, RET, AR, and ABCC4. The present AI/ML computational approach therefore demonstrates that the integration of physicochemical properties and predicted on- and off-target biological interactions can significantly improve DILI predictivity compared to chemical properties alone.

The evolving role of investigative toxicology in the pharmaceutical industry

For decades, preclinical toxicology was essentially a descriptive discipline in which treatment-related effects were carefully reported and used as a basis to calculate safety margins for drug candidates. In recent years, however, technological advances have increasingly enabled researchers to gain insights into toxicity mechanisms, supporting greater understanding of species relevance and translatability to humans, prediction of safety events, mitigation of side effects and development of safety biomarkers. Consequently, investigative (or mechanistic) toxicology has been gaining momentum and is now a key capability in the pharmaceutical industry. Here, we provide an overview of the current status of the field using case studies and discuss the potential impact of ongoing technological developments, based on a survey of investigative toxicologists from 14 European-based medium-sized to large pharmaceutical companies.

Preclinical species gene expression database: Development and meta-analysis

The evaluation of toxicity in preclinical species is important for identifying potential safety liabilities of experimental medicines. Toxicology studies provide translational insight into potential adverse clinical findings, but data interpretation may be limited due to our understanding of cross-species biological differences. With the recent technological advances in sequencing and analyzing omics data, gene expression data can be used to predict cross species biological differences and improve experimental design and toxicology data interpretation. However, interpreting the translational significance of toxicogenomics analyses can pose a challenge due to the lack of comprehensive preclinical gene expression datasets. In this work, we performed RNA-sequencing across four preclinical species/strains widely used for safety assessment (CD1 mouse, Sprague Dawley rat, Beagle dog, and Cynomolgus monkey) in ∼50 relevant tissues/organs to establish a comprehensive preclinical gene expression body atlas for both males and females. In addition, we performed a meta-analysis across the large dataset to highlight species and tissue differences that may be relevant for drug safety analyses. Further, we made these databases available to the scientific community. This multi-species, tissue-, and sex-specific transcriptomic database should serve as a valuable resource to enable informed safety decision-making not only during drug development, but also in a variety of disciplines that use these preclinical species.

Multispectroscopic, virtual and in vivo insights into the photoaging defense mediated by the natural food colorant bixin

An upsurge in early onset of photoaging due to repeated skin exposure to environmental stressors such as UV radiation is a challenge for pharmaceutical and cosmeceutical divisions. Current reports indicate severe side effects because of chemical or synthetic inhibitors of matrix metalloproteases (MMPs) in anti-skin aging cosmeceuticals. We evaluated the adequacy of bixin, a well-known FDA certified food additive, as a scavenger of free radicals and its inhibitory mechanism of action on MMP1, collagenase, elastase, and hyaluronidase. The anti-skin aging potential of bixin was evaluated by several biotechnological tools in silico, in vitro and in vivo. Molecular docking and simulation dynamics studies gave a virtual insight into the robust binding interaction between bixin and skin aging-related enzymes. Absorbance and fluorescence studies, enzyme inhibition assays, enzyme kinetics and in vitro bioassays of human dermal fibroblast (HDF) cells highlighted bixin's role as a potent antioxidant and inhibitor of skin aging-related enzymes. Furthermore, in vivo protocols were carried out to study the impact of bixin administration on UVA induced photoaging in C57BL/6 mice skin. Here, we uncover the UVA shielding effect of bixin and its efficacy as a novel anti-photoaging agent. Furthermore, the findings of this study provide a strong foundation to explore the pharmaceutical applications of bixin in several other biochemical pathways linked to MMP1, collagenase, elastase, and hyaluronidase.

Repurposing drugs to treat cardiovascular disease in the era of precision medicine

Drug repurposing is the use of a given therapeutic agent for indications other than that for which it was originally designed or intended. The concept is appealing because of potentially lower development costs and shorter timelines than are needed to produce a new drug. To date, drug repurposing for cardiovascular indications has been opportunistic and driven by knowledge of disease mechanisms or serendipitous observation rather than by systematic endeavours to match an existing drug to a new indication. Innovations in two areas of personalized medicine - computational approaches to associate drug effects with disease signatures and predictive model systems to screen drugs for disease-modifying activities - support efforts that together create an efficient pipeline to systematically repurpose drugs to treat cardiovascular disease. Furthermore, new experimental strategies that guide the medicinal chemistry re-engineering of drugs could improve repurposing efforts by tailoring a medicine to its new indication. In this Review, we summarize the historical approach to repurposing and discuss the technological advances that have created a new landscape of opportunities.

Why 90% of clinical drug development fails and how to improve it?

Ninety percent of clinical drug development fails despite implementation of many successful strategies, which raised the question whether certain aspects in target validation and drug optimization are overlooked? Current drug optimization overly emphasizes potency/specificity using structure‒activity-relationship (SAR) but overlooks tissue exposure/selectivity in disease/normal tissues using structure‒tissue exposure/selectivity-relationship (STR), which may mislead the drug candidate selection and impact the balance of clinical dose/efficacy/toxicity. We propose structure‒tissue exposure/selectivity-activity relationship (STAR) to improve drug optimization, which classifies drug candidates based on drug's potency/selectivity, tissue exposure/selectivity, and required dose for balancing clinical efficacy/toxicity. Class I drugs have high specificity/potency and high tissue exposure/selectivity, which needs low dose to achieve superior clinical efficacy/safety with high success rate. Class II drugs have high specificity/potency and low tissue exposure/selectivity, which requires high dose to achieve clinical efficacy with high toxicity and needs to be cautiously evaluated. Class III drugs have relatively low (adequate) specificity/potency but high tissue exposure/selectivity, which requires low dose to achieve clinical efficacy with manageable toxicity but are often overlooked. Class IV drugs have low specificity/potency and low tissue exposure/selectivity, which achieves inadequate efficacy/safety, and should be terminated early. STAR may improve drug optimization and clinical studies for the success of clinical drug development.

CNS pharmacology of NKCC1 inhibitors

The Na-K-2Cl cotransporter NKCC1 and the neuron-specific K-Cl cotransporter KCC2 are considered attractive CNS drug targets because altered neuronal chloride regulation and consequent effects on GABAergic signaling have been implicated in numerous CNS disorders. While KCC2 modulators are not yet clinically available, the loop diuretic bumetanide has been used off-label in attempts to treat brain disorders and as a tool for NKCC1 inhibition in preclinical models. Bumetanide is known to have anticonvulsant and neuroprotective effects under some pathophysiological conditions. However, as shown in several species from neonates to adults (mice, rats, dogs, and by extrapolation in humans), at the low clinical doses of bumetanide approved for diuresis, this drug has negligible access into the CNS, reaching levels that are much lower than what is needed to inhibit NKCC1 in cells within the brain parenchyma. Several drug discovery strategies have been initiated over the last ∼15 years to develop brain-permeant compounds that, ideally, should be selective for NKCC1 to eliminate the diuresis mediated by inhibition of renal NKCC2. The strategies employed to improve the pharmacokinetic and pharmacodynamic properties of NKCC1 blockers include evaluation of other clinically approved loop diuretics; development of lipophilic prodrugs of bumetanide; development of side-chain derivatives of bumetanide; and unbiased high-throughput screening approaches of drug discovery based on large chemical compound libraries. The main outcomes are that (1), non-acidic loop diuretics such as azosemide and torasemide may have advantages as NKCC1 inhibitors vs. bumetanide; (2), bumetanide prodrugs lead to significantly higher brain levels than the parent drug and have lower diuretic activity; (3), the novel bumetanide side-chain derivatives do not exhibit any functionally relevant improvement of CNS accessibility or NKCC1 selectivity vs. bumetanide; (4) novel compounds discovered by high-throughput screening may resolve some of the inherent problems of bumetanide, but as yet this has not been achieved. Thus, further research is needed to optimize the design of brain-permeant NKCC1 inhibitors. In parallel, a major challenge is to identify the mechanisms whereby various NKCC1-expressing cellular targets of these drugs within (e.g., neurons, oligodendrocytes or astrocytes) and outside the brain parenchyma (e.g., the blood-brain barrier, the choroid plexus, and the endocrine system), as well as molecular off-target effects, might contribute to their reported therapeutic and adverse effects.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Application of a High-Content Screening Assay Utilizing Primary Human Lung Fibroblasts to Identify Antifibrotic Drugs for Rapid Repurposing in COVID-19 Patients

Lung imaging and autopsy reports among COVID-19 patients show elevated lung scarring (fibrosis). Early data from COVID-19 patients as well as previous studies from severe acute respiratory syndrome, Middle East respiratory syndrome, and other respiratory disorders show that the extent of lung fibrosis is associated with a higher mortality, prolonged ventilator dependence, and poorer long-term health prognosis. Current treatments to halt or reverse lung fibrosis are limited; thus, the rapid development of effective antifibrotic therapies is a major global medical need that will continue far beyond the current COVID-19 pandemic. Reproducible fibrosis screening assays with high signal-to-noise ratios and disease-relevant readouts such as extracellular matrix (ECM) deposition (the hallmark of fibrosis) are integral to any antifibrotic therapeutic development. Therefore, we have established an automated high-throughput and high-content primary screening assay measuring transforming growth factor-β (TGFβ)-induced ECM deposition from primary human lung fibroblasts in a 384-well format. This assay combines longitudinal live cell imaging with multiparametric high-content analysis of ECM deposition. Using this assay, we have screened a library of 2743 small molecules representing approved drugs and late-stage clinical candidates. Confirmed hits were subsequently profiled through a suite of secondary lung fibroblast phenotypic screening assays quantifying cell differentiation, proliferation, migration, and apoptosis. In silico target prediction and pathway network analysis were applied to the confirmed hits. We anticipate this suite of assays and data analysis tools will aid the identification of new treatments to mitigate against lung fibrosis associated with COVID-19 and other fibrotic diseases.

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.

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.

Developing in vitro assays to transform gastrointestinal safety assessment: potential for microphysiological systems

Drug-induced gastrointestinal toxicities (DI-GITs) are among the most common adverse events in clinical trials. High prevalence of DI-GIT has persisted among new drugs due in part to the lack of robust experimental tools to allow early detection or to guide optimization of safer molecules. Developing in vitro assays for the leading GI toxicities (nausea, vomiting, diarrhoea, constipation, and abdominal pain) will likely involve recapitulating complex physiological properties that require contributions from diverse cell/tissue types including epithelial, immune, microbiome, nerve, and muscle. While this stipulation may be beyond traditional 2D monocultures of intestinal cell lines, emerging 3D GI microtissues capture interactions between diverse cell and tissue types. These interactions give rise to microphysiologies fundamental to gut biology. For GI microtissues, organoid technology was the breakthrough that introduced intestinal stem cells with the capability of differentiating into each of the epithelial cell types and that self-organize into a multi-cellular tissue proxy with villus- and crypt-like domains. Recently, GI microtissues generated using miniaturized devices with microfluidic flow and cyclic peristaltic strain were shown to induce Caco2 cells to spontaneously differentiate into each of the principle intestinal epithelial cell types. Second generation models comprised of epithelial organoids or microtissues co-cultured with non-epithelial cell types can successfully reproduce cross-'tissue' functional interactions broadening the potential of these models to accurately study drug-induced toxicities. A new paradigm in which in vitro assays become an early part of GI safety assessment could be realized if microphysiological systems (MPS) are developed in alignment with drug-discovery needs. Herein, approaches for assessing GI toxicity of pharmaceuticals are reviewed and gaps are compared with capabilities of emerging GI microtissues (e.g., organoids, organ-on-a-chip, transwell systems) in order to provide perspective on the assay features needed for MPS models to be adopted for DI-GIT assessment.

Introduction to a manuscript series on the characterization and use of microphysiological systems (MPS) in pharmaceutical safety and ADME applications

Safety related drug failures continue to be a challenge for pharmaceutical companies despite the numerous complex and lengthy in vitro assays and in vivo studies that make up the typical safety screening funnel. A lack of complete translation of animal data to humans can explain some of those shortcomings. Differences in sensitivity and drug disposition between animals and humans may also play a role. Many gaps exist for potential target tissues of drugs that cannot be adequately modeled in vitro. Microphysiological systems (MPS) may help to better model these target tissues and provide an opportunity to better assess some aspects of human safety prior to clinical studies. There is hope that these systems can supplement current preclinical drug safety and disposition evaluations, filling gaps and enhancing our ability to predict and understand human relevant toxicities. The International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) MPS Affiliate is a group of pharmaceutical industry scientists who seek to expedite appropriate characterization and incorporation of MPS to potentially improve drug safety assessment and provide safer and more effective medicines to patients. In keeping with this mission, the IQ MPS Affiliate scientists have prepared a series of organotypic manuscripts for several key drug safety and disposition target tissues (lung, liver, kidney, skin, gastrointestinal, cardiovascular, and blood brain barrier/central nervous system). The goal of these manuscripts is to provide key information related to likely initial contexts of use (CoU) and key characterization data needed for incorporation of MPS in pharmaceutical safety screening including a list of characteristic functions, cell types, toxicities, and test agents (representing major mechanisms of toxicity) that can be used by MPS developers. Additional manuscripts focusing on testing biologically based therapeutics and ADME considerations have been prepared as part of this effort. These manuscripts focus on general needs for assessing biologics and ADME endpoints and include similar information to the tissue specific manuscripts where appropriate. The current manuscript is an introduction to several general concepts related to pharmaceutical industry needs with regard to MPS application and other MPS concepts that apply across the organ specific manuscripts.

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.

Small molecule target identification using photo-affinity chromatography

Identification of the protein targets of bioactive small molecules is a routine challenge in chemical biology and phenotype-based drug discovery. Recent years have seen an explosion of approaches to meeting this challenge, but the traditional method of affinity pulldowns remains a practical choice in many contexts. This technique can be used as long as an affinity probe can be synthesized, usually with a crosslinking moiety to enable photo-affinity pulldowns. It can be applied to varied tissue types and can be performed with minimal specialized equipment. Here, we provide our protocol for photo-affinity pulldown experiments, with notes on making this method generally applicable to varied target identification challenges.