Toxicology Strategies for Drug Discovery: Present and Future

Anxiolytic Efficacy of Indirubin: In Vivo Approach Along with Receptor Binding Profiling and Molecular Interaction with GABAergic Pathways

Anxiety is a natural response to stress, characterized by feelings of worry, fear, or unease. The current research was conducted to investigate the anxiolytic effect of indirubin (IND) in different behavioral paradigms in Swiss albino mice. To observe the animal's behavioural response to assess anxiolytic activity, different tests were performed, such as the open-field (square cross, grooming, and rearing), swing, dark-light, and hole cross tests. The experimental mice were administered IND (5 and 10 mg/kg, p.o.), where diazepam (DZP) and vehicle were used as positive and negative controls, respectively. In addition, a combination treatment (DZP+IND-10) was provided to the animals to determine the modulatory effect of IND on DZP. Molecular docking approach was also conducted to determine the binding energy of IND with the GABAA receptor (α2 and α3 subunits) and pharmacokinetics were also estimated. The findings revealed that IND dose-dependently significantly (p<0.05) reduced the animal's movement exerting calming behavior like DZP. IND also demonstrated the highest docking score (-7.7 kcal/mol) against the α3 subunit, while DZP showed a lower docking value (-6.4 kcal/mol) than IND. The ADMET analysis revealed that IND has proper drug-likeness and pharmacokinetic characteristics. In conclusion, IND exerted anxiolytic effects through GABAergic Pathways.

Piperine exerts anti-inflammatory effects and antagonises the properties of celecoxib and ketoprofen: in vivo and molecular docking studies

This study evaluates the anti-inflammatory effects of a natural product, piperine (PPN), using in vivo and in silico methodologies. In the in vivo segment, inflammation was induced in the right hind paw of young chicks via a formalin (50 μL) injection. PPN was orally administered at doses of 25 and 50 mg/kg with or without celecoxib (CXB) and/or ketoprofen (KPN) (42 mg/kg). The vehicle acted as the negative control group (NC). The in silico analysis predicted the drug-likeness, pharmacokinetics, and toxicity profile of PPN, along with evaluating its binding affinity and ligand-receptor interactions. Results indicate that PPN significantly (p < 0.05) reduced licking frequency and paw edoema in a dose-dependent manner. However, in combination therapy, PPN diminished the effects of both CXB and KPN. PPN showed high affinity (-8.6 kcal/mol) towards the COX-2 enzyme. Therefore, PPN exerts anti-inflammatory effects in chicks through COX-2 inhibition pathways and antagonises CXB and KPN activities.

Safety Implications of Modulating Nuclear Receptors: A Comprehensive Analysis from Non-Clinical and Clinical Perspectives

The unintended modulation of nuclear receptor (NR) activity by drugs can lead to toxicities amongst the endocrine, gastrointestinal, hepatic cardiovascular, and central nervous systems. While secondary pharmacology screening assays include NRs, safety risks due to unintended interactions of small molecule drugs with NRs remain poorly understood. To identify potential nonclinical and clinical safety effects resulting from functional interactions with 44 of the 48 human-expressed NRs, we conducted a systematic narrative review of the scientific literature, tissue expression data, and used curated databases (OFF-X™) (Off-X, Clarivate) to organize reported toxicities linked to the functional modulation of NRs in a tabular and machine-readable format. The top five NRs associated with the highest number of safety alerts from peer-reviewed journals, regulatory agency communications, congresses/conferences, clinical trial registries, and company communications were the Glucocorticoid Receptor (GR, 18,328), Androgen Receptor (AR, 18,219), Estrogen Receptor (ER, 12,028), Retinoic acid receptors (RAR, 10,450), and Pregnane X receptor (PXR, 8044). Toxicities associated with NR modulation include hepatotoxicity, cardiotoxicity, endocrine disruption, carcinogenicity, metabolic disorders, and neurotoxicity. These toxicities often arise from the dysregulation of receptors like Peroxisome proliferator-activated receptors (PPARα, PPARγ), the ER, PXR, AR, and GR. This dysregulation leads to various health issues, including liver enlargement, hepatocellular carcinoma, heart-related problems, hormonal imbalances, tumor growth, metabolic syndromes, and brain function impairment. Gene expression analysis using heatmaps for human and rat tissues complemented the functional modulation of NRs associated with the reported toxicities. Interestingly, certain NRs showed ubiquitous expression in tissues not previously linked to toxicities, suggesting the potential utilization of organ-specific NR interactions for therapeutic purposes.

Synthetic human gonadal tissues for toxicology

The process of mammalian reproduction involves the development of fertile germ cells in the testis and ovary, supported by the surrounders. Fertilization leads to embryo development and ultimately the birth of offspring inheriting parental genome information. Any disruption in this process can result in disorders such as infertility and cancer. Chemical toxicity affecting the reproductive system and embryogenesis can impact birth rates, overall health, and fertility, highlighting the need for animal toxicity studies during drug development. However, the translation of animal data to human health remains challenging due to interspecies differences. In vitro culture systems offer a promising solution to bridge this gap, allowing the study of mammalian cells in an environment that mimics the physiology of the human body. Current advances on in vitro culture systems, such as organoids, enable the development of biomaterials that recapitulate the physiological state of reproductive organs. Application of these technologies to human gonadal cells would provide effective tools for drug screening and toxicity testing, and these models would be a powerful tool to study reproductive biology and pathology. This review focuses on the 2D/3D culture systems of human primary testicular and ovarian cells, highlighting the novel approaches for in vitro study of human reproductive toxicology, specifically in the context of testis and ovary.

Integrated convolution and self-attention for improving peptide toxicity prediction

MOTIVATION

Peptides are promising agents for the treatment of a variety of diseases due to their specificity and efficacy. However, the development of peptide-based drugs is often hindered by the potential toxicity of peptides, which poses a significant barrier to their clinical application. Traditional experimental methods for evaluating peptide toxicity are time-consuming and costly, making the development process inefficient. Therefore, there is an urgent need for computational tools specifically designed to predict peptide toxicity accurately and rapidly, facilitating the identification of safe peptide candidates for drug development.

RESULTS

We provide here a novel computational approach, CAPTP, which leverages the power of convolutional and self-attention to enhance the prediction of peptide toxicity from amino acid sequences. CAPTP demonstrates outstanding performance, achieving a Matthews correlation coefficient of approximately 0.82 in both cross-validation settings and on independent test datasets. This performance surpasses that of existing state-of-the-art peptide toxicity predictors. Importantly, CAPTP maintains its robustness and generalizability even when dealing with data imbalances. Further analysis by CAPTP reveals that certain sequential patterns, particularly in the head and central regions of peptides, are crucial in determining their toxicity. This insight can significantly inform and guide the design of safer peptide drugs.

AVAILABILITY AND IMPLEMENTATION

The source code for CAPTP is freely available at https://github.com/jiaoshihu/CAPTP.

Integrating multiscale and machine learning approaches towards the SAMPL9 log P challenge

The partition coefficient (log P) is an important physicochemical property that provides information regarding a molecule's pharmacokinetics, toxicity, and bioavailability. Methods to accurately predict the partition coefficient have the potential to accelerate drug design. In an effort to test current methods and explore new computational techniques, the statistical assessment of the modeling of proteins and ligands (SAMPL) has established a blind prediction challenge. The ninth iteration challenge was to predict the toluene-water partition coefficient (log Ptol/w) of sixteen drug molecules. Herein, three approaches are reported broadly under the categories of quantum mechanics (QM), molecular mechanics (MM), and data-driven machine learning (ML). The three blind submissions yield mean unsigned errors (MUE) ranging from 1.53-2.93 log Ptol/w units. The MUEs were reduced to 1.00 log Ptol/w for the QM methods. While MM and ML methods outperformed DFT approaches for challenge molecules with fewer rotational degrees of freedom, they suffered for the larger molecules in this dataset. Overall, DFT functionals paired with a triple-ζ basis set were the simplest and most effective tool to obtain quantitatively accurate partition coefficients.

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.

Safety evaluation of saffron extracts in early and established atherosclerotic New Zealand white rabbits

Previous research has shown that natural medications pose health risks, especially in subjects with comorbidities. This study aimed to evaluate the safety of saffron ethanolic extract (SEE) administration in early and established atherosclerotic rabbits. Rabbits were given a high-cholesterol diet (HCD) for 4 and 8 weeks to induce early and established atherosclerosis respectively, and then they were treated with 50 and 100 mg/kg/day SEE. The body weight of the animals was recorded. Blood samples were collected at baseline, pre-treatment, and post-treatment for hematological studies, lipid profiles, and biochemical profiles. Tissue specimens of the vital organs were subjected to histological examination. The above parameters were significantly altered post-intervention with 4 and 8 weeks of HCD. No significant differences in body weight were observed in all the groups post-treatment with 50 and 100mg/kg of SEE compared to pre-treatment. However, low-density lipoprotein cholesterol, total cholesterol, serum urea, and glucose significantly decreased post-treatment with 50 and 100mg/kg/day SEE compared to pre-treatment in early and established atherosclerosis groups. Hematological parameters that were affected post-intervention with HCD returned to their baseline values post-treatment with 50 and 100mg/kg/day SEE. There was a significant improvement in the vital organs post-treatment with 50 and 100mg/kg SEE. SEE can safely be administered without causing harmful effects on the hematological, biochemical profiles, and vital organs. Notably, SEE exerts hypolipidemic and hypoglycemic effects on atherosclerotic conditions. Further clinical trials are warranted to ensure the safety of saffron administration in patients with atherosclerosis-related diseases.

Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer

Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. Fewer than 20% of patients only survive beyond five years from diagnosis. CRC is a highly preventable disease if diagnosed at the early stage of malignancy. Several screening methods like endoscopy (like colonoscopy; gold standard), imaging examination [computed tomographic colonography (CTC)], guaiac-based fecal occult blood (gFOBT), immunochemical test from faeces, and stool DNA test are available with different levels of sensitivity and specificity. The available screening methods are associated with certain drawbacks like invasiveness, cost, or sensitivity. In recent years, computer-aided systems-based screening, diagnosis, and treatment have been very promising in the early-stage detection and diagnosis of CRC cases. Artificial intelligence (AI) is an enormously in-demand, cost-effective technology, that uses various tools machine learning (ML), and deep learning (DL) to screen, diagnose, and stage, and has great potential to treat CRC. Moreover, different ML algorithms and neural networks [artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machines (SVMs)] have been deployed to predict precise and personalized treatment options. This review examines and summarizes different ML and DL models used for therapeutic intervention in CRC cancer along with the gap and challenges for AI.

Inducible mismatch repair streamlines forward genetic approaches to target identification of cytotoxic small molecules

Orphan cytotoxins are small molecules for which the mechanism of action (MoA) is either unknown or ambiguous. Unveiling the mechanism of these compounds may lead to useful tools for biological investigation and new therapeutic leads. In selected cases, the DNA mismatch repair-deficient colorectal cancer cell line, HCT116, has been used as a tool in forward genetic screens to identify compound-resistant mutations, which have ultimately led to target identification. To expand the utility of this approach, we engineered cancer cell lines with inducible mismatch repair deficits, thus providing temporal control over mutagenesis. By screening for compound resistance phenotypes in cells with low or high rates of mutagenesis, we increased both the specificity and sensitivity of identifying resistance mutations. Using this inducible mutagenesis system, we implicate targets for multiple orphan cytotoxins, including a natural product and compounds emerging from a high-throughput screen, thus providing a robust tool for future MoA studies.

Addressing Key Questions in Organoid Models: Who, Where, How, and Why?

Organoids are three-dimensional cellular structures designed to recreate the biological characteristics of the body's native tissues and organs in vitro. There has been a recent surge in studies utilizing organoids due to their distinct advantages over traditional two-dimensional in vitro approaches. However, there is no consensus on how to define organoids. This literature review aims to clarify the concept of organoids and address the four fundamental questions pertaining to organoid models: (i) What constitutes organoids?-The cellular material. (ii) Where do organoids grow?-The extracellular scaffold. (iii) How are organoids maintained in vitro?-Via the culture media. (iv) Why are organoids suitable in vitro models?-They represent reproducible, stable, and scalable models for biological applications. Finally, this review provides an update on the organoid models employed within the female reproductive tract, underscoring their relevance in both basic biology and clinical applications.

Data science in drug discovery safety: Challenges and opportunities

Early de-risking of drug targets and chemistry is essential to provide drug projects with the best chance of success. Target safety assessments (TSAs) use target biology, gene and protein expression data, genetic information from humans and animals, and competitor compound intelligence to understand the potential safety risks associated with modulating a drug target. However, there is a vast amount of information, updated daily that must be considered for each TSA. We have developed a data science-based approach that allows acquisition of relevant evidence for an optimal TSA. This is built on expert-led conventional and artificial intelligence-based mining of literature and other bioinformatics databases. Potential safety risks are identified according to an evidence framework, adjusted to the degree of target novelty. Expert knowledge is necessary to interpret the evidence and to take account of the nuances of drug safety, the modality, and the intended patient population for each TSA within each project. Overall, TSAs take full advantage of the most recent developments in data science and can be used within drug projects to identify and mitigate risks, helping with informed decision-making and resource management. These approaches should be used in the earliest stages of a drug project to guide decisions such as target selection, discovery chemistry options, in vitro assay choice, and end points for investigative in vivo studies.

Computer-assisted drug discovery of potential natural inhibitors of the SARS-CoV-2 RNA-dependent RNA polymerase through a multi-phase in silico approach

BACKGROUND

The COVID-19 pandemic has led to significant loss of life and economic disruption worldwide. Currently, there are limited effective treatments available for this disease. SARS-CoV-2 RNA-dependent RNA polymerase (SARS-CoV-2 RdRp) has been identified as a potential target for drug development against COVID-19. Natural products have been shown to possess antiviral properties, making them a promising source for developing drugs against SARS-CoV-2.

OBJECTIVES

The objective of this study is to identify the most effective natural inhibitors of SARS-CoV-2 RdRp among a set of 4924 African natural products using a multi-phase in silico approach.

METHODS

The study utilized remdesivir (RTP), the co-crystallized ligand of RdRp, as a starting point to select compounds that have the most similar chemical structures among the examined set of compounds. Molecular fingerprints and structure similarity studies were carried out in the first part of the study. The second part of the study included molecular docking against SARS-CoV-2 RdRp (PDB ID: 7BV2) and Molecular Dynamics (MD) simulations including the calculation of RMSD, RMSF, Rg, SASA, hydrogen bonding, and PLIP. Moreover, the calculations of Molecular mechanics with generalised Born and surface area solvation (MM-GBSA) Lennard-Jones and Columbic electrostatic interaction energies have been conducted. Additionally, in silico ADMET and toxicity studies were performed to examine the drug likeness degrees of the selected compounds.

RESULTS

Eight compounds were identified as the most effective natural inhibitors of SARS-CoV-2 RdRp. These compounds are kaempferol 3-galactoside, kaempferol 3-O-β-D-glucopyranoside, mangiferin methyl ether, luteolin 7-O-β-D-glucopyranoside, quercetin-O-β-D-3-glucopyranoside, 1-methoxy-3-indolylmethyl glucosinolate, naringenin, and asphodelin A 4'-O-β-D-glucopyranoside.

CONCLUSION

The results of this study provide valuable information for the development of natural product-based drugs against COVID-19. However, the elected compounds should be further studied in vitro and in vivo to confirm their efficacy in treating COVID-19.

Development of a fluorescent-labeled trapping reagent to evaluate the risk posed by acyl-CoA conjugates

Although acyl-CoA conjugates are known to have higher reactivity than acyl glucuronides, few studies have been conducted to evaluate the risk of the conjugates. In the present study, we aimed to develop a trapping assay for acyl-CoA conjugates using trapping reagents we have developed previously. It was revealed that Cys-Dan, which has both a thiol and an amino group, was the most effective in forming stable adducts containing an amide bond after intramolecular acyl migration. Additionally, we also developed a hepatocyte-based trapping assay in the present study to overcome the shortcomings of liver microsomes. Although liver microsomes are commonly used as enzyme sources in trapping assays, they lack some of the enzymes required for drug metabolism and detoxification systems. In human hepatocytes, our three trapping reagents, CysGlu-Dan, Dap-Dan and Cys-Dan, captured CYP-dependent reactive metabolites, reactive acyl glucuronides, and reactive acyl-CoA conjugates, respectively. The work suggests that the trapping assay with the reagents in hepatocytes is useful to evaluate the risk of reactive metabolites in drug discovery.

Toxicogenomics Approaches to Address Toxicity and Carcinogenicity in the Liver

Toxicogenomic technologies query the genome, transcriptome, proteome, and the epigenome in a variety of toxicological conditions. Due to practical considerations related to the dynamic range of the assays, sensitivity, cost, and technological limitations, transcriptomic approaches are predominantly used in toxicogenomics. Toxicogenomics is being used to understand the mechanisms of toxicity and carcinogenicity, evaluate the translational relevance of toxicological responses from in vivo and in vitro models, and identify predictive biomarkers of disease and exposure. In this session, a brief overview of various transcriptomic technologies and practical considerations related to experimental design was provided. The advantages of gene network analyses to define mechanisms were also discussed. An assessment of the utility of toxicogenomic technologies in the environmental and pharmaceutical space showed that these technologies are being increasingly used to gain mechanistic insights and determining the translational relevance of adverse findings. Within the environmental toxicology area, there is a broader regulatory consideration of benchmark doses derived from toxicogenomics data. In contrast, these approaches are mainly used for internal decision-making in pharmaceutical development. Finally, the development and application of toxicogenomic signatures for prediction of apical endpoints of regulatory concern continues to be area of intense research.

Off target toxicities and links with physicochemical properties of medicinal products, including antibiotics, oligonucleotides, lipid nanoparticles (with cationic and/or anionic charges). Data review suggests an emerging pattern

Toxicology is an essential part of any drug development plan. Circumnavigating the risk of failure because of a toxicity issue can be a challenge, and failure in late development is extremely costly. To identify potential risks, it requires more than just understanding the biological target. The toxicologist needs to consider a compound's structure, it's physicochemical properties (including the impact of the overall formulation), as well as the biological target (e.g., receptor interactions). Understanding the impact of the physicochemical properties can be used to predict potential toxicities in advance by incorporating key endpoints in early screening strategies and/or used to compare toxicity profiles across lead candidates. This review discussed the risks of off-target and/or non-specific toxicities that may be associated with the physicochemical properties of compounds, especially those carrying dominant positive or negative charges, including amphiphilic small molecules, peptides, oligonucleotides and lipids/liposomes/lipid nanoparticles. The latter of which are being seen more and more in drug development, including the recent Covid pandemic, where mRNA and lipid nanoparticle technology is playing more of a role in vaccine development. The translation between non-clinical and clinical data is also considered, questioning how a physicochemical driven toxicity may be more universal across species, which means that such toxicity may be reassuringly translatable between species and as such, this information may also be considered as a support to the 3 R's, particularly in the early screening stages of a drug development plan.

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 Combination of a Human Biomimetic Liver Microphysiology System with BIOLOGXsym, a Quantitative Systems Toxicology (QST) Modeling Platform for Macromolecules, Provides Mechanistic Understanding of Tocilizumab- and GGF2-Induced Liver Injury

Biologics address a range of unmet clinical needs, but the occurrence of biologics-induced liver injury remains a major challenge. Development of cimaglermin alfa (GGF2) was terminated due to transient elevations in serum aminotransferases and total bilirubin. Tocilizumab has been reported to induce transient aminotransferase elevations, requiring frequent monitoring. To evaluate the clinical risk of biologics-induced liver injury, a novel quantitative systems toxicology modeling platform, BIOLOGXsym™, representing relevant liver biochemistry and the mechanistic effects of biologics on liver pathophysiology, was developed in conjunction with clinically relevant data from a human biomimetic liver microphysiology system. Phenotypic and mechanistic toxicity data and metabolomics analysis from the Liver Acinus Microphysiology System showed that tocilizumab and GGF2 increased high mobility group box 1, indicating hepatic injury and stress. Tocilizumab exposure was associated with increased oxidative stress and extracellular/tissue remodeling, and GGF2 decreased bile acid secretion. BIOLOGXsym simulations, leveraging the in vivo exposure predicted by physiologically-based pharmacokinetic modeling and mechanistic toxicity data from the Liver Acinus Microphysiology System, reproduced the clinically observed liver signals of tocilizumab and GGF2, demonstrating that mechanistic toxicity data from microphysiology systems can be successfully integrated into a quantitative systems toxicology model to identify liabilities of biologics-induced liver injury and provide mechanistic insights into observed liver safety signals.

Inducible mismatch repair streamlines forward genetic approaches to target identification of cytotoxic small molecules

Orphan cytotoxins are small molecules for which the mechanism of action (MoA) is either unknown or ambiguous. Unveiling the mechanism of these compounds may lead to useful tools for biological investigation and in some cases, new therapeutic leads. In select cases, the DNA mismatch repair-deficient colorectal cancer cell line, HCT116, has been used as a tool in forward genetic screens to identify compound-resistant mutations, which have ultimately led to target identification. To expand the utility of this approach, we engineered cancer cell lines with inducible mismatch repair deficits, thus providing temporal control over mutagenesis. By screening for compound resistance phenotypes in cells with low or high rates of mutagenesis, we increased both the specificity and sensitivity of identifying resistance mutations. Using this inducible mutagenesis system, we implicate targets for multiple orphan cytotoxins, including a natural product and compounds emerging from a high-throughput screen, thus providing a robust tool for future MoA studies.

A review of therapeutic failures in late-stage clinical trials

INTRODUCTION

The process of drug approval involves extensive and expensive preclinical and clinical examination. Most drugs entering late-stage clinical trials get terminated for a variety of reasons including inability to achieve the primary endpoints or intolerable adverse effects. Only one-tenth of the drugs that enter clinical trials progress to Food and Drug Administration (FDA) regulatory submission.

AREAS COVERED

This review offers insight into some of the attributes that may be responsible for a drug's failure in late-stage trials. Information from multiple open sources including PubMed articles published between 1989 and 2019, recent articles from authentic websites like www.ClinicalTrials.gov, www.fda.gov, and pharmaceutical news articles for the years between 2017 and 2021 were accumulated and summarized. Further, a few drug candidates that reached the phase III clinical trials but were discontinued at later stages have been presented as case studies.

EXPERT OPINION

Ineluctable failures were observed due to insufficient knowledge about the mechanism of action where the disease progression stages are unclear. Other reasons were choice of patient population, late-stage treatment, and dosage. Adhering to the guidelines and recommendations provided by the regulatory authorities and learning from past failures, considerably reduce failure rates.

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.

In Silico Identification of Multi-Target Ligands as Promising Hit Compounds for Neurodegenerative Diseases Drug Development

The conventional treatment of neurodegenerative diseases (NDDs) is based on the "one molecule-one target" paradigm. To combat the multifactorial nature of NDDs, the focus is now shifted toward the development of small-molecule-based compounds that can modulate more than one protein target, known as "multi-target-directed ligands" (MTDLs), while having low affinity for proteins that are irrelevant for the therapy. The in silico approaches have demonstrated a potential to be a suitable tool for the identification of MTDLs as promising drug candidates with reduction in cost and time for research and development. In this study more than 650,000 compounds were screened by a series of in silico approaches to identify drug-like compounds with predicted activity simultaneously towards three important proteins in the NDDs symptomatic treatment: acetylcholinesterase (AChE), histone deacetylase 2 (HDAC2), and monoamine oxidase B (MAO-B). The compounds with affinities below 5.0 µM for all studied targets were additionally filtered to remove known non-specifically binding or unstable compounds. The selected four hits underwent subsequent refinement through in silico blood-brain barrier penetration estimation, safety evaluation, and molecular dynamics simulations resulting in two hit compounds that constitute a rational basis for further development of multi-target active compounds against NDDs.

Exploratory toxicology studies of 2,3-substituted imidazo[1,2-a]pyridines with antiparasitic and anti-inflammatory properties

Background

Trichomoniasis and amoebiasis are neglected diseases and still remain as a global health burden not only for developing countries, from where are endemic, but also for the developed world. Previously, we tested the antiparasitic activity of a number of imidazo[1,2-a]pyridine derivatives (IMPYs) on metronidazole-resistant strains of Entamoeba Hystolitica (HM1:IMSS), and Trichomonas Vaginalis (GT3). Their anti-inflammatory activity was also evaluated.

Objective

The present work is a part of a project whose aim is to find new alternatives to standard treatments for these maladies, and to address the current concern of emerging resistant parasite strains. Here we report a non-clinical study focused on exploratory toxicology assays of seven IMPYs that showed the best antiparasitic and/or anti-inflammatory properties.

Methods

Acute, and subacute toxicity tests were carried out. After 14-day oral treatment, liver and kidney functionality assays in combination with chemometric methods were implemented to detect hepatic and/or kidney damage.

Results

Some compounds produced off-target effects. Vehicle effects were also detected. However, no signs of hepatic or renal toxicity were observed for any IMPY.

Conclusion

These compounds can continue non-clinical evaluations, and if possible, clinical trials as new candidates to treat trichomoniasis and amoebiasis, and inflammatory diseases. Further studies are also needed to fully elucidate a proposed dual effect that may exert these molecules against trichomoniasis and amoebiasis, which may also signify a novel mechanism of action to treat these infections.

Trends in small molecule drug properties: A developability molecule assessment perspective

Developability molecule assessment is a key interfacial capability across the biopharmaceutical industry, screening and staging molecules discovered by medicinal chemists for successful chemistry manufacturing controls (CMC) development and launch. The breadth of responsibility and expertise such teams possess puts them in a unique position to understand the impact of the physicochemical properties of a drug during its initial discovery and subsequent development. However, most of the publications describing trends in physicochemical properties are written from a medicinal chemistry perspective with the aim to identify molecules with better ADMET profiles that are either lead-like or drug-like, failing to describe the impact these properties have on CMC development. To systematically uncover knowledge obtained from recent trends in physicochemical properties and the corresponding impact on CMC development, a comprehensive analysis was conducted on molecules in the drug repurposing hub dataset. The only physicochemical property that seems to have been preserved in FDA-approved oral molecules over the decades (1900-2020) is a constant H-bond donor count, highlighting the importance this property has on cell permeability and lattice energy. Pharmaceutical attrition analysis suggests that partition-distribution coefficient, H-bond acceptors, polar surface area and the fraction of sp³ carbons are properties that are associated with compound attrition. Looking at pharmaceutical attrition asynchronously with the temporal analysis of FDA-approved oral molecules highlights the opposing trends, risks and diminishing effects some of these physiochemical properties (cLogP, cLogD and Fsp3) have on describing compound attrition during the past decade. Trellising the dataset by target class suggests that certain formulation and drug delivery strategies can be anticipated or put into place based on target class of a molecule. For example, molecules binding to nuclear hormone receptors are amenable to lipid-based drug delivery systems with proven commercial success. Although the poor solubility of kinase inhibitors is a combination of hydrophobicity (due to aromaticity) required to bind to its target and high lattice energy (melting point), they are a challenging target class to formulate. The influence of drug targets on physicochemical properties and the temporal nature of these properties is highlighted when comparing molecules in the drug repurposing dataset to those developed at Amgen. An improved understanding of the impact of molecular properties on performance attributes can accelerate decisions and facilitate risk assessments during candidate selection and development.

Molecular dynamics simulations reveal the inhibitory mechanism of Withanolide A against α-glucosidase and α-amylase

Diabetes mellitus (DM) is a global chronic disease characterized by hyperglycemia and insulin resistance. The unsavory severe gastrointestinal side-effects of synthetic drugs to regulate hyperglycemia have warranted the search for alternative treatments to inhibit the carbohydrate digestive enzymes (e.g. α-amylase and α-glucosidase). Certain phytochemicals recently captured the scientific community's attention as carbohydrate digestive enzyme inhibitors due to their low toxicity and high efficacy, specifically the Withanolides-loaded extract of Withania somnifera. That said, the present study evaluated in silico the efficacy of Withanolide A in targeting both α-amylase and α-glucosidase in comparison to the synthetic drug Acarbose. Protein-ligand interactions, binding affinity, and stability were characterized using pharmacological profiling, high-end molecular docking, and molecular-dynamic simulation. Withanolide A inhibited the activity of α-glucosidase and α-amylase better, exhibiting good pharmacokinetic properties, absorption, and metabolism. Also, Withanolide A was minimally toxic, with higher bioavailability. Interestingly, Withanolide A bonded well to the active site of α-amylase and α-glucosidase, yielding the lowest binding free energy of -82.144 ± 10.671 kcal/mol and -102.1043 ± 11.231 kcal/mol compared to the Acarbose-enzyme complexes (-63.220 ± 13.283 kcal/mol and -82.148 ± 10.671 kcal/mol). Hence, the findings supported the therapeutic potential of Withanolide A as α-amylase and α-glucosidase inhibitor for DM treatment.Communicated by Ramaswamy H. Sarma.

A Comprehensive Evaluation of Sdox, a Promising H2S-Releasing Doxorubicin for the Treatment of Chemoresistant Tumors

Sdox is a hydrogen sulfide (H₂S)-releasing doxorubicin effective in P-glycoprotein-overexpressing/doxorubicin-resistant tumor models and not cytotoxic, as the parental drug, in H9c2 cardiomyocytes. The aim of this study was the assessment of Sdox drug-like features and its absorption, distribution, metabolism, and excretion (ADME)/toxicity properties, by a multi- and transdisciplinary in silico, in vitro, and in vivo approach. Doxorubicin was used as the reference compound. The in silico profiling suggested that Sdox possesses higher lipophilicity and lower solubility compared to doxorubicin, and the off-targets prediction revealed relevant differences between Dox and Sdox towards several cancer targets, suggesting different toxicological profiles. In vitro data showed that Sdox is a substrate with lower affinity for P-glycoprotein, less hepatotoxic, and causes less oxidative damage than doxorubicin. Both anthracyclines inhibited CYP3A4, but not hERG currents. Unlike doxorubicin, the percentage of zebrafish live embryos at 72 hpf was not affected by Sdox treatment. In conclusion, these findings demonstrate that Sdox displays a more favorable drug-like ADME/toxicity profile than doxorubicin, different selectivity towards cancer targets, along with a greater preclinical efficacy in resistant tumors. Therefore, Sdox represents a prototype of innovative anthracyclines, worthy of further investigations in clinical settings.

Induction of Liver Size Reduction in Zebrafish Larvae by the Emerging Synthetic Cannabinoid 4F-MDMB-BINACA and Its Impact on Drug Metabolism

Zebrafish (ZF; Danio rerio) larvae have become a popular in vivo model in drug metabolism studies. Here, we investigated the metabolism of methyl 2-[1-(4-fluorobutyl)-1H-indazole-3-carboxamido]-3,3-dimethylbutanoate (4F-MDMB-BINACA) in ZF larvae after direct administration of the cannabinoid via microinjection, and we visualized the spatial distributions of the parent compound and its metabolites by mass spectrometry imaging (MSI). Furthermore, using genetically modified ZF larvae, the role of cannabinoid receptor type 1 (CB1) and type 2 (CB2) on drug metabolism was studied. Receptor-deficient ZF mutant larvae were created using morpholino oligonucleotides (MOs), and CB2-deficiency had a critical impact on liver development of ZF larva, leading to a significant reduction of liver size. A similar phenotype was observed when treating wild-type ZF larvae with 4F-MDMB-BINACA. Thus, we reasoned that the cannabinoid-induced impaired liver development might also influence its metabolic function. Studying the metabolism of two synthetic cannabinoids, 4F-MDMB-BINACA and methyl 2-(1-(5-fluoropentyl)-1H-pyrrolo[2,3-b]pyridine-3-carboxamido)-3,3-dimethylbutanoate (7′N-5F-ADB), revealed important insights into the in vivo metabolism of these compounds and the role of cannabinoid receptor binding.

Nonclinical Safety Pharmacology Study of the Herbal Product HAD-B1

HAD-B1 is a Korean herbal formula designed to treat solid tumors, and through cell experiments, it has proven to have an anticancer effect. The current study aims to test the safety of HAD-B1. This experiment is under the regulation of ICH. In order to find if HAD-B1 has any effect on the CNS, 0, 500, 1000, and 2000 mg/kg/day of HAD-B1 were orally administered to male and female rats once. To discover any effect on the respiratory system, 0, 500, 1000, and 2000 mg/kg/day of HAD-B1 were orally given to male rats followed by measuring the respiratory rate, tidal volume, and minute respiratory volume. To assess the possibility of a delayed QT period as a result of the drug administration, hERG analysis was conducted at 0, 0.1, 0.3, and 1 μg/ml. To assess any effect on the cardiovascular system, 0, 500, 1000, and 2000 mg/kg/day of HAD-B1 were orally given to male beagle dogs once followed by temperature, blood pressure, ECG, and heart rate analyses. There were no clinically significant changes in both male and female rats on assessing any effects on the CNS. There were no clinically significant changes in male rats' respiratory assessment. There were no clinically significant changes in hERG analysis results. There were no clinically significant changes in the cardiovascular system of male beagle dogs. Our results demonstrate that HAD-B1 is a safe herbal formula that does not have a clinically significant effect on the CNS, respiratory, and cardiovascular systems.

Natural-product-inspired design and synthesis of two series of compounds active against Trypanosoma cruzi: Insights into structure-activity relationship, toxicity, and mechanism of action

Chemical scaffolds of natural products have historically been sources of inspiration for the development of novel molecules of biological relevance, including hit and lead compounds. To identify new compounds active against Trypanosoma cruzi, we designed and synthesized 46 synthetic derivatives based on the structure of two classes of natural products: tetrahydrofuran lignans (Series 1) and oxazole alkaloids (Series 2). Compounds were screened in vitro using a cellular model of T. cruzi infection. In the first series of compounds, 11 derivatives of hit compound 5 (EC₅₀ = 1.1 µM) were found to be active; the most potent (7, 8, and 13) had EC₅₀ values of 5.1-34.2 µM. In the second series, 17 analogs were found active at 50 µM; the most potent compounds (47, 49, 59, and 63) showed EC₅₀ values of 24.2-49.1 µM. Active compounds were assessed for selectivity, hemocompatibility, synergistic potential, effects on mitochondrial membrane potential, and inhibitory effect on trypanothione reductase. All active compounds showed low toxicity against uninfected THP-1 cells and human erythrocytes. The potency of compounds 5 and 8 increased steadily in combination with benznidazole, indicating a synergistic effect. Furthermore, compounds 8, 47, 49, 59, and 63 inhibited parasitic mitochondria in a dose-dependent manner. Although increased reactive oxygen species levels might lead to mitochondrial effects, the results indicate that the mechanism of action of the compounds is not dependent on trypanothione reductase inhibition. In silico calculation of chemical descriptors and principal component analysis showed that the active compounds share common chemical features with other trypanocidal molecules and are predicted to have a good ADMET profile. Overall, the results suggest that the compounds are important candidates to be further studied for their potential against T. cruzi.

Development of a Fluorescent-Labeled Trapping Reagent to Detect Reactive Acyl Glucuronides

Acyl glucuronides are common metabolites of carboxylic acid-containing compounds. Since acyl glucuronides sometimes show high reactivity, they are considered to be involved in drug toxicity. Therefore, it is important to evaluate the risk posed by acyl glucuronides in the development of safe drugs; however, there are no suitable evaluation methods for the early stages of drug discovery. We aimed to develop a trapping reagent that detects reactive acyl glucuronides to assess their risk. We designed a diamine-structured trapping reagent, Dap-Dan, and compared its trapping ability with the reported one that has an amino group, and results showed that Dap-Dan showed higher accuracy. In the trapping assay with 17 medicines containing a carboxylic acid, Dap-Dan trapped acyl glucuronides that had a higher risk of toxicity. In conclusion, Dap-Dan can be useful for evaluating the risk of reactive acyl glucuronides.

Hydrosoluble and Liposoluble Vitamins: New Perspectives through ADMET Analysis

Background and Objectives: The present study demonstrates that apart from the well-known toxicity of liposoluble vitamins, some hydrosoluble vitamins may also exert toxicity; thus, routine supplementation with vitamins or ingestion of fortified foods should not be considered harmless. The study addresses the possible correlations between the physico-chemical properties and the side effects of vitamins when taken in high doses or for a too long a period. Materials and Methods: The FAFDrugs4.0 computational tool was used for computational assessment of the ADMET profile of several hydro- and liposoluble vitamins. Results: ADMET analysis revealed the following major data: vitamin B3 and B13 showed reduced structural complexity; thus, a relative toxicological potential may be exerted. Vitamins B1 and B7 were found to have good oral absorption and thus good bioavailability, while Vitamin B3 was found to have decreased oral absorption. In addition, all of the liposoluble vitamins reflected higher complexity, much greater than most of the potentially therapeutically-proven compounds. Conclusions: The present study emphasizes the importance between the physico-chemical properties of vitamins and their possible toxicological impact.

Mollusc-Derived Brominated Indoles for the Selective Inhibition of Cyclooxygenase: A Computational Expedition

Inflammation plays an important role in different chronic diseases. Brominated indoles derived from the Australian marine mollusk Dicathais orbita (D. orbita) are of interest for their anti-inflammatory properties. This study evaluates the binding mechanism and potentiality of several brominated indoles (tyrindoxyl sulfate, tyrindoleninone, 6-bromoisatin, and 6,6′-dibromoindirubin) against inflammatory mediators cyclooxygenases-1/2 (COX-1/2) using molecular docking, followed by molecular dynamics simulation, along with physicochemical, drug-likeness, pharmacokinetic (pk), and toxicokinetic (tk) properties. Molecular docking identified that these indole compounds are anchored, with the main amino acid residues, positioned in the binding pocket of the COX-1/2, required for selective inhibition. Moreover, the molecular dynamics simulation based on root mean square deviation (RMSD), radius of gyration (Rg), solvent accessible surface area (SASA), and root mean square fluctuation (RMSF) analyses showed that these natural brominated molecules transit rapidly to a progressive constant configuration during binding with COX-1/2 and seem to accomplish a consistent dynamic behavior by maintaining conformational stability and compactness. The results were comparable to the Food and Drug Administration (FDA)-approved selective COX inhibitor, aspirin. Furthermore, the free energy of binding for the compounds assessed by molecular mechanics–Poisson–Boltzmann surface area (MM–PBSA) confirmed the binding capacity of indoles towards COX-1/2, with suitable binding energy values except for the polar precursor tyrindoxyl sulfate (with COX-1). The physicochemical and drug-likeness analysis showed zero violations of Lipinski’s rule, and the compounds are predicted to have excellent pharmacokinetic profiles. These indoles are projected to be non-mutagenic and free from hepatotoxicity, with no inhibition of human ether-a-go–go gene (hERG) I inhibitors, and the oral acute toxicity LD₅₀ in rats is predicted to be similar or lower than aspirin. Overall, this work has identified a plausible mechanism for selective COX inhibition by natural marine indoles as potential therapeutic candidates for the mitigation of inflammation.

Bioisosteres of the Phenyl Ring: Recent Strategic Applications in Lead Optimization and Drug Design

The benzene moiety is the most prevalent ring system in marketed drugs, underscoring its historic popularity in drug design either as a pharmacophore or as a scaffold that projects pharmacophoric elements. However, introspective analyses of medicinal chemistry practices at the beginning of the 21st century highlighted the indiscriminate deployment of phenyl rings as an important contributor to the poor physicochemical properties of advanced molecules, which limited their prospects of being developed into effective drugs. This Perspective deliberates on the design and applications of bioisosteric replacements for a phenyl ring that have provided practical solutions to a range of developability problems frequently encountered in lead optimization campaigns. While the effect of phenyl ring replacements on compound properties is contextual in nature, bioisosteric substitution can lead to enhanced potency, solubility, and metabolic stability while reducing lipophilicity, plasma protein binding, phospholipidosis potential, and inhibition of cytochrome P450 enzymes and the hERG channel.

A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges

Traditionally, in vitro and in vivo methods are useful for estimating human pharmacokinetics (PK) parameters; however, it is impractical to perform these complex and expensive experiments on a large number of compounds. The integration of publicly available chemical, or medical Big Data and artificial intelligence (AI)-based approaches led to qualitative and quantitative prediction of human PK of a candidate drug. However, predicting drug response with these approaches is challenging, partially because of the adaptation of algorithmic and limitations related to experimental data. In this report, we provide an overview of machine learning (ML)-based quantitative structure-activity relationship (QSAR) models used in the assessment or prediction of PK values as well as databases available for obtaining such data.

Zebrafish toxicological screening could aid Leishmaniosis drug discovery

BACKGROUND

Recently a screen from a library of 1.8 million compounds identified in vitro a potent activity of the 2-aminobenzimidazoles series against Leishmania infantum, the etiological agent responsible by over 20.000 deaths each year. Several analogs were synthesized and in vitro tested through an optimization program, leading to a promising 2-aminobenzimidazoles derived compound (2amnbzl-d) that was progressed to in vivo mice studies. However, the not expected toxic effects prevented its progression to more advanced preclinical and clinical phases of drug development. Due to limitations of cell models in detecting whole organism complex interactions, 90% of the compounds submitted to pre-clinical tests are reproved. The use of Zebrafish embryo models could improve this rate, saving mammals, time and costs in the development of new drugs. To test this hypothesis, we compared 2amnbzl-d with two compounds with already established safety profile: carbamazepine and benznidazole, using an embryo Zebrafish platform based on acute toxicity, hepatotoxicity, neurotoxicity and cardiotoxicity assays (Pltf-AcHpNrCd).

RESULTS

Tests were performed blindly, and the results demonstrated the presence of lethal and teratogenic effects (CL50%: 14.8 µM; EC50%: 8.6 µM), hepatotoxic in concentrations above 7.5 µM and neurotoxic in embryos exposed to 15 µM of 2amnbzl-d. Nevertheless, benznidazole exposition showed no toxicity and only the 100 µM of carbamazepine induced a bradycardia.

CONCLUSIONS

Results using Pltf-AcHpNrCd with zebrafish reproduced that found in the toxicological tests with mammals to a portion of the costs and time of experimentation.

Preclinical Toxicity and Safety of MM-129-First-in-Class BTK/PD-L1 Inhibitor as a Potential Candidate against Colon Cancer

MM-129 is a novel inhibitor targeting BTK/PI3K/AKT/mTOR and PD-L1, as it possesses antitumor activity against colon cancer. To evaluate the safety profile of MM-129, we conducted a toxicity study using the zebrafish and rodent model. MM-129 was also assessed for pharmacokinetics features through an in vivo study on Wistar rats. The results revealed that MM-129 exhibited favorable pharmacokinetics with quick absorption and 68.6% of bioavailability after intraperitoneal administration. No serious adverse events were reported for the use of MM-129, confirming a favorable safety profile for this compound. It was not fatal and toxic to mice at an anticancer effective dose of 10 μmol/kg. At the end of 14 days of administering hematological and biochemical parameters, liver and renal functions were all at normal levels. No sublethal effects were either detected in zebrafish embryos treated with a concentration of 10 μM. MM-129 has the potential as a safe and well-tolerated anticancer formulation for future treatment of patients with colon cancer.

Target-Based Evaluation of "Drug-Like" Properties and Ligand Efficiencies

Physicochemical descriptors commonly used to define "drug-likeness" and ligand efficiency measures are assessed for their ability to differentiate marketed drugs from compounds reported to bind to their efficacious target or targets. Using ChEMBL version 26, a data set of 643 drugs acting on 271 targets was assembled, comprising 1104 drug-target pairs having ≥100 published compounds per target. Taking into account changes in their physicochemical properties over time, drugs are analyzed according to their target class, therapy area, and route of administration. Recent drugs, approved in 2010-2020, display no overall differences in molecular weight, lipophilicity, hydrogen bonding, or polar surface area from their target comparator compounds. Drugs are differentiated from target comparators by higher potency, ligand efficiency (LE), lipophilic ligand efficiency (LLE), and lower carboaromaticity. Overall, 96% of drugs have LE or LLE values, or both, greater than the median values of their target comparator compounds.

Advances in Applying Computer-Aided Drug Design for Neurodegenerative Diseases

Neurodegenerative diseases (NDs) including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease are incurable and affect millions of people worldwide. The development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools.

Network Pharmacology and Molecular Docking Suggest the Mechanism for Biological Activity of Rosmarinic Acid

Rosmarinic acid (RosA) is a natural phenolic acid compound, which is mainly extracted from Labiatae and Arnebia. At present, there is no systematic analysis of its mechanism. Therefore, we used the method of network pharmacology to analyze the mechanism of RosA. In our study, PubChem database was used to search for the chemical formula and the Chemical Abstracts Service (CAS) number of RosA. Then, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to evaluate the pharmacodynamics of RosA, and the Comparative Toxicogenomics Database (CTD) was used to identify the potential target genes of RosA. In addition, the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of target genes were carried out by using the web-based gene set analysis toolkit (WebGestalt). At the same time, we uploaded the targets to the STRING database to obtain the protein interaction network. Then, we carried out a molecular docking about targets and RosA. Finally, we used Cytoscape to establish a visual protein-protein interaction network and drug-target-pathway network and analyze these networks. Our data showed that RosA has good biological activity and drug utilization. There are 55 target genes that have been identified. Then, the bioinformatics analysis and network analysis found that these target genes are closely related to inflammatory response, tumor occurrence and development, and other biological processes. These results demonstrated that RosA can act on a variety of proteins and pathways to form a systematic pharmacological network, which has good value in drug development and utilization.

Flavonoids and hERG channels: Friends or foes?

The cardiac action potential is regulated by several ion channels. Drugs capable to block these channels, in particular the human ether-à-go-go-related gene (hERG) channel, also known as K_V11.1 channel, may lead to a potentially lethal ventricular tachyarrhythmia called "Torsades de Pointes". Thus, evaluation of the hERG channel off-target activity of novel chemical entities is nowadays required to safeguard patients as well as to avoid attrition in drug development. Flavonoids, a large class of natural compounds abundantly present in food, beverages, herbal medicines, and dietary food supplements, generally escape this assessment, though consumed in consistent amounts. Continuously growing evidence indicates that these compounds may interact with the hERG channel and block it. The present review, by examining numerous studies, summarizes the state-of-the-art in this field, describing the most significant examples of direct and indirect inhibition of the hERG channel current operated by flavonoids. A description of the molecular interactions between a few of these natural molecules and the Rattus norvegicus channel protein, achieved by an in silico approach, is also presented.

Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods

Computational methods to predict molecular properties regarding safety and toxicology represent alternative approaches to expedite drug development, screen environmental chemicals, and thus significantly reduce associated time and costs. There is a strong need and interest in the development of computational methods that yield reliable predictions of toxicity, and many approaches, including the recently introduced deep neural networks, have been leveraged towards this goal. Herein, we report on the collection, curation, and integration of data from the public data sets that were the source of the ChemIDplus database for systemic acute toxicity. These efforts generated the largest publicly available such data set comprising > 80,000 compounds measured against a total of 59 acute systemic toxicity end points. This data was used for developing multiple single- and multitask models utilizing random forest, deep neural networks, convolutional, and graph convolutional neural network approaches. For the first time, we also reported the consensus models based on different multitask approaches. To the best of our knowledge, prediction models for 36 of the 59 end points have never been published before. Furthermore, our results demonstrated a significantly better performance of the consensus model obtained from three multitask learning approaches that particularly predicted the 29 smaller tasks (less than 300 compounds) better than other models developed in the study. The curated data set and the developed models have been made publicly available at https://github.com/ncats/ld50-multitask, https://predictor.ncats.io/, and https://cactus.nci.nih.gov/download/acute-toxicity-db (data set only) to support regulatory and research applications.

Neurotoxicity and underlying cellular changes of 21 mitochondrial respiratory chain inhibitors

Inhibition of complex I of the mitochondrial respiratory chain (cI) by rotenone and methyl-phenylpyridinium (MPP +) leads to the degeneration of dopaminergic neurons in man and rodents. To formally describe this mechanism of toxicity, an adverse outcome pathway (AOP:3) has been developed that implies that any inhibitor of cI, or possibly of other parts of the respiratory chain, would have the potential to trigger parkinsonian motor deficits. We used here 21 pesticides, all of which are described in the literature as mitochondrial inhibitors, to study the general applicability of AOP:3 or of in vitro assays that are assessing its activation. Five cI, three complex II (cII), and five complex III (cIII) inhibitors were characterized in detail in human dopaminergic neuronal cell cultures. The NeuriTox assay, examining neurite damage in LUHMES cells, was used as in vitro proxy of the adverse outcome (AO), i.e., of dopaminergic neurodegeneration. This test provided data on whether test compounds were unspecific cytotoxicants or specifically neurotoxic, and it yielded potency data with respect to neurite degeneration. The pesticide panel was also examined in assays for the sequential key events (KE) leading to the AO, i.e., mitochondrial respiratory chain inhibition, mitochondrial dysfunction, and disturbed proteostasis. Data from KE assays were compared to the NeuriTox data (AO). The cII-inhibitory pesticides tested here did not appear to trigger the AOP:3 at all. Some of the cI/cIII inhibitors showed a consistent AOP activation response in all assays, while others did not. In general, there was a clear hierarchy of assay sensitivity: changes of gene expression (biomarker of neuronal stress) correlated well with NeuriTox data; mitochondrial failure (measured both by a mitochondrial membrane potential-sensitive dye and a respirometric assay) was about 10–260 times more sensitive than neurite damage (AO); cI/cIII activity was sometimes affected at > 1000 times lower concentrations than the neurites. These data suggest that the use of AOP:3 for hazard assessment has a number of caveats: (i) specific parkinsonian neurodegeneration cannot be easily predicted from assays of mitochondrial dysfunction; (ii) deriving a point-of-departure for risk assessment from early KE assays may overestimate toxicant potency.

Comparison of 21 data-rich mitochondrial toxicants for neurotoxicity

Quantitative comparison of key event triggering thresholds for AOP:3

Comparison of two cell models and two exposure times for neurotoxicity

Comparison of transcriptome changes and classical key event measures for sensitivity

Improving target assessment in biomedical research: the GOT-IT recommendations

Academic research plays a key role in identifying new drug targets, including understanding target biology and links between targets and disease states. To lead to new drugs, however, research must progress from purely academic exploration to the initiation of efforts to identify and test a drug candidate in clinical trials, which are typically conducted by the biopharma industry. This transition can be facilitated by a timely focus on target assessment aspects such as target-related safety issues, druggability and assayability, as well as the potential for target modulation to achieve differentiation from established therapies. Here, we present recommendations from the GOT-IT working group, which have been designed to support academic scientists and funders of translational research in identifying and prioritizing target assessment activities and in defining a critical path to reach scientific goals as well as goals related to licensing, partnering with industry or initiating clinical development programmes. Based on sets of guiding questions for different areas of target assessment, the GOT-IT framework is intended to stimulate academic scientists' awareness of factors that make translational research more robust and efficient, and to facilitate academia-industry collaboration.

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.

Artificial intelligence in the early stages of drug discovery

Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there is no unique solution for this need for innovation, there has recently been a strong interest in the use of Artificial Intelligence for this purpose. As a matter of fact, not only there have been major contributions from the scientific community in this respect, but there has also been a growing partnership between the pharmaceutical industry and Artificial Intelligence companies. Beyond these contributions and efforts there is an underlying question, which we intend to discuss in this review: can the intrinsic difficulties within the drug discovery process be overcome with the implementation of Artificial Intelligence? While this is an open question, in this work we will focus on the advantages that these algorithms provide over the traditional methods in the context of early drug discovery.

Early preclinical screening using zebrafish (Danio rerio) reveals the safety of the candidate anti-inflammatory therapeutic agent TnP

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TnP has been indicated for chronic inflammatory diseases, multiple sclerosis.

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Zebrafish is an alternative animal model for preclinical drug development.

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Preclinical toxicology studies have shown that TnP has a wide therapeutic index range from 1 nM to 10 μM.

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TnP did not induce cardiotoxic effect or cardiac dysfunction.

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TnP crossed the blood-brain barrier without causing neurotoxicity.

The patented anti-inflammatory peptide TnP had its effectiveness recently confirmed in vivo in a murine model of multiple sclerosis and asthma. In this work, the safety of the TnP was evaluated in investigative toxicology tests using zebrafish (Danio rerio) as a model. We conducted the OECD #236 test to investigate effects of the TnP on the survival, hatching performance, and morphological formation of zebrafish embryos. After determining these endpoints, morphometric analysis termination of locomotion eartbeat rate in zebrafish larvae were evaluated to identify adverse effects such as neurotoxicity and cardiotoxicity. The results highlight a wide therapeutic index for TnP with non-lethal and safe doses rom 1 nM to 10 μM, without causing neurotoxicity or cardiotoxic effect. The low frequencyf abnormalities by TnP was associated with high safety of the molecule and the developing embryo's ability to process and eliminate it. TnP crossed the blood-brain barrier without disturbing the normal architecture of forebrain, midbrain and hindbrain. Our data reinforce the importance of zebrafish as an accurate investigative toxicology model to assess acute toxicity as well as cardiotoxicity and neurotoxicity of molecules in the preclinical phase of development.