Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction

Artificial intelligence revolution in drug discovery: A paradigm shift in pharmaceutical innovation

Integrating artificial intelligence (AI) into drug discovery has revolutionized pharmaceutical innovation, addressing the challenges of traditional methods that are costly, time-consuming, and suffer from high failure rates. By utilizing machine learning (ML), deep learning (DL), and natural language processing (NLP), AI enhances various stages of drug development, including target identification, lead optimization, de novo drug design, and drug repurposing. AI tools, such as AlphaFold for protein structure prediction and AtomNet for structure-based drug design, have significantly accelerated the discovery process, improved efficiency and reduced costs. Success stories like Insilico Medicine's AI-designed molecule for idiopathic pulmonary fibrosis and BenevolentAI's identification of baricitinib for COVID-19 highlight AI's transformative potential. Additionally, AI enables the exploration of vast chemical spaces, optimization of clinical trials, and the identification of novel therapeutic targets, paving the way for precision medicine. However, challenges such as limited data accessibility, integration of diverse datasets, interpretability of AI models, and ethical concerns remain critical hurdles. Overcoming these limitations through enhanced algorithms, standardized databases, and interdisciplinary collaboration is essential. Overall, AI continues to reshape drug discovery, reducing timelines, increasing success rates, and driving the development of innovative and accessible therapies for unmet medical needs.

Cholinesterase activity modulators: Evaluation of dodecylaminoquinuclidines as inhibitors of human AChE and BChE

In this study, we investigated the inhibitory activity of novel dodecylaminoquinuclidines (QAs) on neurotransmitter-hydrolyzing enzymes, specifically acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). Following our previous findings, we modified the structure of a lead compound to develop more potent modulators of cholinesterase activity. The search for such inhibitors remains a key focus in the therapeutic management of organophosphate poisoning and various neurological disorders. For the design, we retained the aliphatic side linker and introduced various benzyl-based substituents to the quinuclidinium core, resulting in a set of 11 new compounds. All of these derivatives exhibited reversible inhibition of cholinesterase within the micromolar concentration range. The most significant factor affecting inhibition was the positional change of a specific group on the benzene ring, shifting from the meta to the para position. Specifically, analogues with groups in the meta position showed a stronger inhibition of AChE, whereas the para position was more effective for BChE. The most potent inhibitor featured a -CH3 or -Br substituent, with a Ki of around 1.7-2.0 μM (meta-position) for AChE and 0.3-0.5 μM (para-position) for BChE. Additionally, we assessed the cytotoxicity of these compounds on human neuronal SH-SY5Y cells, as their intended target in the body. As all tested quinuclidine derivatives demonstrated certain level of cytotoxicity within the range of 1.5-17 μM, further research is needed to explore this effect, and to validate or negate their potential for development into therapeutic agents.

Fate-tox: fragment attention transformer for E(3)-equivariant multi-organ toxicity prediction

Toxicity is a critical hurdle in drug development, often causing the late-stage failure of promising compounds. Existing computational prediction models often focus on single-organ toxicity. However, avoiding toxicity of an organ, such as reducing gastrointestinal side effects, may inadvertently lead to toxicity in another organ, as seen in the real case of rofecoxib, which was withdrawn due to increased cardiovascular risks. Thus, simultaneous prediction of multi-organ toxicity is a desirable but challenging task. The main challenges are (1) the variability of substructures that contribute to toxicity of different organs, (2) insufficient power of molecular representations in diverse perspectives, and (3) explainability of prediction results especially in terms of substructures or potential toxicophores. To address these challenges with multiple strategies, we developed FATE-Tox, a novel multi-view deep learning framework for multi-organ toxicity prediction. For variability of substructures, we used three fragmentation methods such as BRICS, Bemis-Murcko scaffolds, and RDKit Functional Groups to formulate fragment-level graphs so that diverse substructures can be used to identify toxicity for different organs. For insufficient power of molecular representations, we used molecular representations in both 2D and 3D perspectives. For explainability, our fragment attention transformer identifies potential 3D toxicophores using attention coefficients. Scientific contribution: Our framework achieved significant improvements in prediction performance, with up to 3.01% gains over prior baseline methods on toxicity benchmark datasets from MoleculeNet (BBBP, SIDER, ClinTox) and TDC (DILI, Skin Reaction, Carcinogens, and hERG), while the multi-task learning approach further enhanced performance by up to 1.44% compared to the single-task learning framework that had already surpassed these baselines. Additionally, attention visualization aligning with literature contributes to greater transparency in predictive modeling. Our approach has the potential to provide scientists and clinicians with a more interpretable and clinically meaningful tool to assess systemic toxicity, ultimately supporting safer and more informed drug development processes.

A clinical chemical atlas of xenobiotic toxicity for the Sprague-Dawley rat

The Consortium for Metabonomic Toxicology (COMET) studies was designed to model metabolic responses to organ- and mechanism-specific toxins to predict acute drug toxicity in rats. A range of clinical chemical parameters were measured in 7-day toxicology studies for 86 toxins eliciting a range of organ- and mechanism-specific effects. Additionally, 21 surgical or physiological stressors were evaluated to identify physiological or metabolic responses that might confound the interpretation of observed toxicity profiles. From these studies on a total of 3473 rats measured at six pharmaceutical companies, we provide a set of 12 serum and 5 urine physical and clinical chemistry parameters. Samples were collected at 24 h, 48 h and 168 h post-dose for each animal and are presented as a downloadable database file. We also summarise the main observations based on the group response at the level of the individual toxin. We demonstrate that correlations between parameters, such as serum bilirubin and aspartate aminotransferase (AST), provide a more nuanced profile of organ-specific toxicity than consideration of individual parameters alone. In addition, we highlight the variability in the measured parameters across the dataset attributable to inter-laboratory differences, and the heterogeneity of metabolic responses to particular compounds or differences in temporal patterns of response. This clinical chemistry atlas of toxicity serves as a valuable reference tool for evaluating the potential toxicity of novel drug candidates.

Mechanisms and therapeutic strategies for NLRP3 degradation via post-translational modifications in ubiquitin-proteasome and autophagy lysosomal pathway

The NLRP3 inflammasome is crucial for immune responses. However, its overactivation can lead to severe inflammatory diseases, underscoring its importance as a target for therapeutic intervention. Although numerous inhibitors targeting NLRP3 exist, regulating its degradation offers an alternative and promising strategy to suppress its activation. The degradation of NLRP3 is primarily mediated by the proteasomal and autophagic pathways. The review not only elaborates on the traditional concepts of ubiquitination and NLRP3 degradation but also investigates the important roles of indirect regulatory modifications, such as phosphorylation, acetylation, ubiquitin-like modifications, and palmitoylation-key post-translational modifications (PTMs) that influence NLRP3 degradation. Additionally, we also discuss the potential targets that may affect NLRP3 degradation during the proteasomal and autophagic pathways. By unraveling these complex regulatory mechanisms, the review aims to enhance the understanding of NLRP3 regulation and its implications for developing therapeutic strategies to combat inflammatory diseases.

Snake Venom Peptide Fractions from Bothrops jararaca and Daboia siamensis Exhibit Differential Neuroprotective Effects in Oxidative Stress-Induced Zebrafish Models

Introduction: Snake venoms are rich sources of bioactive peptides with therapeutic potential, particularly against neurodegenerative diseases linked to oxidative stress. While the peptide fraction (<10 kDa) from Bothrops jararaca venom has shown in vitro neuroprotection, analogous fractions from related species remain unexplored in vivo. Methods: This study comparatively evaluated the neuroprotective effects of two peptide fractions (pf) from Daboia siamensis (pf-Ds) and B. jararaca (pf-Bj) against H2O2-induced oxidative stress using in vitro (PC12 cells) and in vivo (zebrafish, Danio rerio) models. Results: In vitro, pf-Ds (1 µg mL-1) did not protect PC12 cells against H2O2-induced cytotoxicity, unlike previously reported effects of pf-Bj. In vivo, neither pf-Ds nor pf-Bj (1-20 µg mL-1) induced significant developmental toxicity in zebrafish larvae up to 120 h post-fertilization (hpf). The neuroprotective effects of both pf were evaluated using two experimental models: (I) Larvae at 96 hpf were exposed to either pf-Ds or pf-Bj (10 µg mL-1) for 4 h, followed by co-exposure to H2O2 (0.2 mmol L-1) for an additional 10 h to induce oxidative stress (4-20 h model); (II) Embryos at 4 hpf were treated with pf-Ds or pf-Bj (10 µg mL-1) continuously until 96 hpf, after which they were exposed to H2O2 (0.2 mmol L-1) for another 24 h (96-120 h model). In a short-term treatment model, neither fraction reversed H2O2-induced deficits in metabolism or locomotor activity. However, in a prolonged treatment model, pf-Bj significantly reversed the H2O2-induced locomotor impairment, whereas pf-Ds did not confer protection. Conclusions: These findings demonstrate, for the first time, the in vivo neuroprotective potential of pf-Bj against oxidative stress-induced behavioral deficits in zebrafish, contingent on the treatment regimen. The differential effects between pf-Ds and pf-Bj highlight species-specific venom composition and underscore the value of zebrafish for evaluating venom-derived peptides.

Exploring bioactive molecules released during inter- and intraspecific competition: A paradigm for novel antiparasitic drug discovery and design for human use

Many antiparasitic drugs have become obsolete and ineffective in treating parasitic diseases. This ineffectiveness arises from parasite drug resistance, high toxicity, and low drug efficacy. Thus, the discovery of novel agents is urgently needed to control parasitic diseases. Various strategies are employed in drug discovery, design, and development. This review highlights the paradigm of searching for bioactive molecules produced during inter- and intraspecific competition among organisms, particularly between microbes and parasites, as a strategy for de novo antiparasitic drug discovery. Competitive interactions occur when individuals of the same or different species coexist in overlapping niches and compete for space and resources. These interactions are well recognized. Therefore, bioactive molecules released during these interactions are promising targets for novel drug discovery. Compelling data indicate that microbes remain a potential source for the discovery of novel antiparasitic drugs because of their diversity. Many antimicrobial producers in nature have yet to be isolated and investigated. This body of evidence underscores the success of numerous therapeutic drugs, including penicillin, β-lactams, and tetracyclines, which have been successfully discovered and developed for treating infectious diseases. This review comprehensively covers these concepts, with a particular focus on inter- and intraspecific competition in the discovery of novel antiparasitic agents. This approach will pave the way for identifying alternative strategies to control and eradicate parasitic diseases that continue to threaten human health. Additionally, this review discusses current antiparasitic drugs and their mechanisms of action, limitations, and existing gaps. This discussion emphasizes the ongoing need to explore novel antiparasitic drugs.

Computational analysis of antimicrobial peptides targeting key receptors in infection-related cardiovascular diseases: molecular docking and dynamics insights

Infection-related cardiovascular diseases (CVDs) pose a significant health challenge, driving the need for novel therapeutic strategies to target key receptors involved in inflammation and infection. Antimicrobial peptides (AMPs) show the potential to disrupt pathogenic processes and offer a promising approach to CVD treatment. This study investigates the binding potential of selected AMPs with critical receptors implicated in CVDs, aiming to explore their therapeutic potential. A comprehensive computational approach was employed to assess AMP interactions with CVD-related receptors, including ACE2, CRP, MMP9, NLRP3, and TLR4. Molecular docking studies identified AMPs with high binding affinities to these targets, notably Tachystatin, Pleurocidin, and Subtilisin A, which showed strong interactions with ACE2, CRP, and MMP9. Following docking, 100 ns molecular dynamics (MD) simulations confirmed the stability of AMP-receptor complexes, and MM/PBSA calculations provided quantitative insights into binding energies, underscoring the potential of these AMPs to modulate receptor activity in infection and inflammation contexts. The study highlights the therapeutic potential of Tachystatin, Pleurocidin, and Subtilisin A in targeting infection-related pathways in CVDs. These AMPs demonstrate promising receptor binding properties and stability in computational models. Future research should focus on in vitro and in vivo studies to confirm their efficacy and safety, paving the way for potential clinical applications in managing infection-related cardiovascular conditions.

Synthesis, Characterization, and Biological Activity of New 4'-Functionalized Bis-Terpyridine Ruthenium(II) Complexes: Anti-Inflammatory Activity Advances

Ruthenium complexes incorporating 2,2' : 6',2''-terpyridine ligands have emerged as promising candidates due to their versatile biological activities including DNA-binding, anti-inflammatory, antimicrobial, and anticancer properties. In this study, three new 4'-functionalized bis(terpyridine) Ru(II) complexes were synthesized. These complexes feature one ligand as 4-(2,2' : 6',2''-terpyridine-4'-yl) benzoic acid and the second ligand as either 4'-(2-thienyl)-2,2' : 6',2''-terpyridine, 4'-(3,4-dimethoxyphenyl)-2,2' : 6',2''-terpyridine, or 4'-(4-dimethylaminophenyl)-2,2' : 6',2''-terpyridine. Besides the chemical characterization by 1H and 13C NMR, mass spectrometry, and absorption and emission spectroscopy, the complexes were tested for their biological activity as anti-inflammatory, anticancer, and antimicrobial agents. Moreover, the toxicity of the Ru(II) complexes was assessed and benchmarked against diclofenac potassium and ibuprofen using a haemolysis assay. Biological evaluations demonstrate that these ruthenium complexes exhibit promising therapeutic potential with reduced haemolytic activity compared to standard drugs. They demonstrate substantial anti-inflammatory effects through inhibition of albumin denaturation along with moderate cytotoxicity against cancer cell lines and broad-spectrum antimicrobial activity. These findings highlight the multifaceted biomedical applications of 4'-functionalized bis(terpyridine) Ru(II) complexes, suggesting their potential for further development as effective and safe therapeutic agents.

Computational insights into marine natural products as potential antidiabetic agents targeting the SIK2 protein kinase domain

Diabetes mellitus (DM) affects over 77 million adults in India, with cases expected to reach 134 million by 2045. Current treatments, including sulfonylureas and thiazolidinediones, are inadequate, underscoring the need for novel therapeutic strategies. This study investigates marine natural products (MNPs) as alternative therapeutic agents targeting SIK2, a key enzyme involved in DM. The structural stability of the predicted SIK2 model was validated using computational methods and subsequently employed for structure-based virtual screening (SBVS) of over 38,000 MNPs. This approach identified five promising candidates: CMNPD21753 and CMNPD13370 from the Comprehensive Marine Natural Product Database, MNPD10685 from the Marine Natural Products Database, and SWMDRR053 and SWMDRR052 from the Seaweed Metabolite Database. The identified compounds demonstrated docking scores ranging from -7.64 to -11.95 kcal/mol and MMGBSA binding scores between -33.29 and -68.29 kcal/mol, with favourable predicted pharmacokinetic and toxicity profiles. Molecular dynamics simulations (MDS) revealed stronger predicted binding affinity for these compounds compared to ARN-3236, a known SIK2 inhibitor. Principal component (PC)-based free energy landscape (FEL) analysis further supported the stable binding of these compounds to SIK2. These computational findings highlight the potential of these leads as novel SIK2 inhibitors, warranting future in vitro and in vivo validation.

Bioactive compounds from fermented Vernonia amygdalina leaf: Potent antibiotics against multidrug-resistant Escherichia coli and Salmonella typhi

Antibiotic resistance microorganisms (ARMs), particularly gram-negative bacteria, pose a global health threat. The effects of fermentation on phytochemicals are numerous, and exploring this potential is the focus of drug development. The study investigated the role of fermentation in modifying V. amygdalina leaf secondary metabolites as an effective antibiotic against Escherichia. coli, Bacillus subtilis and Salmonella typhi. This work showed that fermentation increased the content of lycopene, flavonoid and carotenoid compounds but decreased chlorophyll, soluble protein and phenol. Pearson's correlation heatmap showed a strong correlation between microbial activities and secondary metabolic changes. The methanolic extract of fermented V. amygdalina leaf pulp (at day 9) showed significant antioxidant and anti-inflammatory activities. The GCMS and FTIR results showed unique compounds and structural modifications at different intervals of the fermentation period. In-vitro and in-silico analyses showed that fermentation did not alter the inhibition rate against B. subtilis; however, E. coli and S. typhi were significantly inhibited by fermented V. amygdalina pulp extracts. In-silico analyses showed that 4,6-Cholestadien-3β-ol- a compound present only on the ninth day of fermentation-was responsible for the inhibition of the gram-negative bacteria via the substitution of multiple non-ionic interactions of some key catalytic site residues with non-ionic types, thereby denying ionisation and salt-bridge properties that porins explore to resist antibiotics; and higher binding affinity to OmpC and OmpF than ampicillin. Therefore, this steroid-derived compound may open a new pipeline for developing ion-independent multi-target antibiotics against broad-spectrum multidrug-resistant gram-positive and gram-negative bacteria in food and pharmaceutical purposes.

Supplementary information

The online version contains supplementary material available at 10.1007/s40203-024-00277-2.

Recent Advances in Omics, Computational Models, and Advanced Screening Methods for Drug Safety and Efficacy

It is imperative to comprehend the mechanisms that underlie drug toxicity in order to enhance the efficacy and safety of novel therapeutic agents. The capacity to identify molecular pathways that contribute to drug-induced toxicity has been significantly enhanced by recent developments in omics technologies, such as transcriptomics, proteomics, and metabolomics. This has enabled the early identification of potential adverse effects. These insights are further enhanced by computational tools, including quantitative structure-activity relationship (QSAR) analyses and machine learning models, which accurately predict toxicity endpoints. Additionally, technologies such as physiologically based pharmacokinetic (PBPK) modeling and micro-physiological systems (MPS) provide more precise preclinical-to-clinical translation, thereby improving drug safety assessments. This review emphasizes the synergy between sophisticated screening technologies, in silico modeling, and omics data, emphasizing their roles in reducing late-stage drug development failures. Challenges persist in the integration of a variety of data types and the interpretation of intricate biological interactions, despite the progress that has been made. The development of standardized methodologies that further enhance predictive toxicology is contingent upon the ongoing collaboration between researchers, clinicians, and regulatory bodies. This collaboration ensures the development of therapeutic pharmaceuticals that are more effective and safer.