Revolutionizing pharmacokinetics: the dawn of AI-powered analysis

This editorial explores how artificial intelligence (AI) is revolutionizing the science of pharmacokinetics (PK). It discusses the challenges of conventional PK analysis and how AI has transformed this area. It highlights the promise of artificial intelligence (AI) in predicting pharmacokinetic profiles from chemical structures and its application in several aspects of pharmacology, including dosage customization and drug interactions. Additionally, it emphasizes how important ethical issues and openness are to AI applications, especially when it comes to pharmacokinetic prediction and dataset adaptation. Future directions for AI in PK are discussed, with the creation of all-inclusive AI pharmacokinetics/pharmacometrics software being envisioned. Drug discovery and patient care could be transformed toward more individualized and effective healthcare solutions with the help of this software, which could handle tasks such as data cleaning, model selection, and regulatory report preparation. The editorial highlights the importance of AI in improving pharmaceutical sciences while urging caution and teamwork in navigating its possible uses in pharmacokinetics.

Carbazole derivatives as promising competitive and allosteric inhibitors of human serotonin transporter: computational pharmacology

The human serotonin transporters (hSERTs) are neurotransmitter sodium symporters of the aminergic G protein-coupled receptors, regulating the synaptic serotonin and neuropharmacological processes related to neuropsychiatric disorders, notably, depression. Selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine and (S)-citalopram are competitive inhibitors of hSERTs and are commonly the first-line medications for major depressive disorder (MDD). However, treatment-resistance and unpleasant aftereffects constitute their clinical drawbacks. Interestingly, vilazodone emerged with polypharmacological (competitive and allosteric) inhibitions on hSERTs, amenable to improved efficacy. However, its application usually warrants adjuvant/combination therapy, another subject of critical adverse events. Thus, the discovery of alternatives with polypharmacological potentials (one-drug-multiple-target) and improved safety remains essential. In this study, carbazole analogues from chemical libraries were explored using docking and molecular dynamics (MD) simulation. Selectively, two IBScreen ligands, STOCK3S-30866 and STOCK1N-37454 predictively bound to the active pockets and expanded boundaries (extracellular vestibules) of the hSERTs more potently than vilazodone and (S)-citalopram. For instance, the two ligands showed docking scores of -9.52 and -9.59 kcal/mol and MM-GBSA scores of -92.96 and -65.66 kcal/mol respectively compared to vilazodone's respective scores of -7.828 and -59.27 against the central active site of the hSERT (PDB 7LWD). Similarly, the two ligands also docked to the allosteric pocket (PDB 5I73) with scores of -8.15 and -8.40 kcal/mol and MM-GBSA of -96.14 and -68.46 kcal/mol whereas (S)-citalopram has -6.90 and -69.39 kcal/mol respectively. The ligands also conferred conformational stability on the receptors during 100 ns MD simulations and displayed interesting ADMET profiles, representing promising hSERT modulators for MDD upon experimental validation.Communicated by Ramaswamy H. Sarma.

Systems and in vitro pharmacology profiling of diosgenin against breast cancer

Aim: The purpose of this study was to establish a mode of action for diosgenin against breast cancer employing a range of system biology tools and to corroborate its results with experimental facts. Methodology: The diosgenin-regulated domains implicated in breast cancer were enriched in the Kyoto Encyclopedia of Genes and Genomes database to establish diosgenin-protein(s)-pathway(s) associations. Later, molecular docking and the lead complexes were considered for molecular dynamics simulations, MMPBSA, principal component, and dynamics cross-correlation matrix analysis using GROMACS v2021. Furthermore, survival analysis was carried out for the diosgenin-regulated proteins that were anticipated to be involved in breast cancer. For gene expression analyses, the top three targets with the highest binding affinity for diosgenin and tumor expression were examined. Furthermore, the effect of diosgenin on cell proliferation, cytotoxicity, and the partial Warburg effect was tested to validate the computational findings using functional outputs of the lead targets. Results: The protein-protein interaction had 57 edges, an average node degree of 5.43, and a p-value of 3.83e-14. Furthermore, enrichment analysis showed 36 KEGG pathways, 12 cellular components, 27 molecular functions, and 307 biological processes. In network analysis, three hub proteins were notably modulated: IGF1R, MDM2, and SRC, diosgenin with the highest binding affinity with IGF1R (binding energy -8.6 kcal/mol). Furthermore, during the 150 ns molecular dynamics (MD) projection run, diosgenin exhibited robust intermolecular interactions and had the least free binding energy with IGF1R (-35.143 kcal/mol) compared to MDM2 (-34.619 kcal/mol), and SRC (-17.944 kcal/mol). Diosgenin exhibited the highest cytotoxicity against MCF7 cell lines (IC₅₀ 12.05 ± 1.33) µg/ml. Furthermore, in H₂O₂-induced oxidative stress, the inhibitory constant (IC₅₀ 7.68 ± 0.51) µg/ml of diosgenin was lowest in MCF7 cell lines. However, the reversal of the Warburg effect by diosgenin seemed to be maximum in non-cancer Vero cell lines (EC₅₀ 15.27 ± 0.95) µg/ml compared to the rest. Furthermore, diosgenin inhibited cell proliferation in SKBR3 cell lines more though. Conclusion: The current study demonstrated that diosgenin impacts a series of signaling pathways, involved in the advancement of breast cancer, including FoxO, PI3K-Akt, p53, Ras, and MAPK signaling. Additionally, diosgenin established a persistent diosgenin-protein complex and had a significant binding affinity towards IGF1R, MDM2, and SRC. It is possible that this slowed down cell growth, countered the Warburg phenomenon, and showed the cytotoxicity towards breast cancer cells.

Machine learning prediction of side effects for drugs in clinical trials

Early and accurate detection of side effects is critical for the clinical success of drugs under development. Here, we aim to predict unknown side effects for drugs with a small number of side effects identified in randomized controlled clinical trials. Our machine learning framework, the geometric self-expressive model (GSEM), learns globally optimal self-representations for drugs and side effects from pharmacological graph networks. We show the usefulness of the GSEM on 505 therapeutically diverse drugs and 904 side effects from multiple human physiological systems. Here, we also show a data integration strategy that could be adopted to improve the ability of side effect prediction models to identify unknown side effects that might only appear after the drug enters the market.

A review of machine learning approaches for drug synergy prediction in cancer

Combinational pharmacotherapy with the synergistic/additive effect is a powerful treatment strategy for complex diseases such as malignancies. Identifying synergistic combinations with various compounds and structures requires testing a large number of compound combinations. However, in practice, examining different compounds by in vivo and in vitro approaches is costly, infeasible and challenging. In the last decades, significant success has been achieved by expanding computational methods in different pharmacological and bioinformatics domains. As promising tools, computational approaches such as machine learning algorithms (MLAs) are used for prioritizing combinational pharmacotherapies. This review aims to provide the models developed to predict synergistic drug combinations in cancer by MLAs with various information, including gene expression, protein-protein interactions, metabolite interactions, pathways and pharmaceutical information such as chemical structure, molecular descriptor and drug-target interactions.

Antihyperlipidemic effect of selected pyrimidine derivatives mediated through multiple pathways

Hyperlipidemia is worth-mentioning risk factor in quickly expanding atherosclerosis, myocardial infarction, and stroke. This study attempted to determine effectiveness of selected pyrimidine derivatives: 5-(3-Hydroxybenzylidene)-2, 4, 6(1H, 3H, 5H)-pyrimidinetrione (SR-5), 5-(4-Hydroxybenzylidene)-2, 4, 6(1H, 3H, 5H)-pyrimidinetrione (SR-8), 5-(3-Chlorobenzylidene)-2, 4, 6(1H, 3H, 5H)-pyrimidinetrione (SR-9), and 5-(4-Chlorobenzylidene)-2, 4, 6(1H, 3H, 5H)-pyrimidinetrione (SR-10) against hyperlipidemia. In silico results revealed that SR-5, SR-8, SR-9, and SR-10 exhibited high affinity with 3-hydroxy-3-methylglutaryl coenzyme A (HMGCoA) possessing binding energy values of -8.2, -8.4, -8.6, and -9.5 Kcal/mol, respectively, and moderate (<-8 Kcal/mol) against other selected targets. In vivo findings showed that test drugs (25 and 50 mg/Kg) significantly decreased HFD rat total cholesterol, triglycerides, low-density lipoprotein, very-low-density lipoprotein, atherogenic index, coronary risk index, alkaline phosphatase, aspartate transaminase, alanine transaminase, and bilirubin and increased high-density lipoprotein (p < 0.05, p < 0.01, p < 0.001 vs HFD group). In animal liver tissues, SR-5, SR-8, SR-9, and SR-10 inhibited HMGCoA reductase enzyme, enhanced glutathione-s-transferase, reduced glutathione, catalase levels, improved cellular architecture in histopathological examination, and decreased expression of inflammatory markers: cyclo-oxygenase 2, tumor necrosis factor alpha, phosphorylated c-Jun N-terminal kinase, and phosphorylated-nuclear factor kappa B, evidenced in immunohistochemistry and enzyme-linked immunosorbent assay molecular investigations. This study indicates that SR-5, SR-8, SR-9, and SR-10 exhibit antihyperlipidemic action, mediated possibly through HMGCoA inhibition, hepatoprotection, antioxidant, and anti-inflammatory pathways.

Computational and Pharmacological Investigation of (E)-2-(4-Methoxybenzylidene)Cyclopentanone for Therapeutic Potential in Neurological Disorders

Purpose

This study involved the computational and pharmacological evaluation of (E)-2-(4-methoxybenzylidene)cyclopentan-1-one (A2K10).

Methods

In silico studies were conducted through virtual screening. Morris water and Y-maze tests were conducted to evaluate Alzheimer’s disease. Acute epilepsy haloperidol,and hyperalgesia were used to calculate the epilepsy model, with Parkinson’s disease and mechanical allodynia at a dose of 1–10 mg/kg in the mouse model.

Results

A2K10 exhibited the highest binding affinity against α₇ nicotinic acetylcholine receptors (−256.02 kcal/mol). A2K10 decreased escape latency in the Morris water test during different trials. In the Y-maze test, A2K10 dose-dependently increased spontaneous alteration behavior, with maximum effect of 75.5%±0.86%. Furthermore, A2K10 delayed onset of pentylenetetrazole-induced myoclonic jerks and tonic–clonic seizures and decreased duration of tonic–clonic convulsions in mice, with maximum effect of 93.8±5.30, 77.8±2.91, and 12.9±1.99 seconds, respectively. In the haloperidol-induced Parkinson’s disease model, A2K10 significantly prolonged hanging time and reduced tardive dyskinesia. Moreover, A2K10 extended latency in hot-plate hyperalgesia and increased the paw-withdrawal threshold in mechanical allodynia. In toxicity studies, no mortality was observed.

Conclusion

Overall, the results indicated that A2K10 has potential as an anti-Alzheimer’s, antiepileptic, antiparkinsonian, and analgesic therapeutic compound.

Identifying Protein Features Responsible for Improved Drug Repurposing Accuracies Using the CANDO Platform: Implications for Drug Design

Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 2030 indications/diseases using 3733 drugs/compounds to predict interactions with 46,784 proteins and relating them via proteomic interaction signatures. The accuracy is calculated by comparing interaction similarities of drugs approved for the same indications. We performed a unique subset analysis by breaking down the full protein library into smaller subsets and then recombining the best performing subsets into larger supersets. Up to 14% improvement in accuracy is seen upon benchmarking the supersets, representing a 100⁻1000-fold reduction in the number of proteins considered relative to the full library. Further analysis revealed that libraries comprised of proteins with more equitably diverse ligand interactions are important for describing compound behavior. Using one of these libraries to generate putative drug candidates against malaria, tuberculosis, and large cell carcinoma results in more drugs that could be validated in the biomedical literature compared to using those suggested by the full protein library. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in drug repurposing, with implications for drug design and machine learning approaches to improve the CANDO platform.

A Systematic Review of Computational Drug Discovery, Development, and Repurposing for Ebola Virus Disease Treatment

Ebola virus disease (EVD) is a deadly global public health threat, with no currently approved treatments. Traditional drug discovery and development is too expensive and inefficient to react quickly to the threat. We review published research studies that utilize computational approaches to find or develop drugs that target the Ebola virus and synthesize its results. A variety of hypothesized and/or novel treatments are reported to have potential anti-Ebola activity. Approaches that utilize multi-targeting/polypharmacology have the most promise in treating EVD.

Predicting protein-ligand affinity with a random matrix framework

Rapid determination of whether a candidate compound will bind to a particular target receptor remains a stumbling block in drug discovery. We use an approach inspired by random matrix theory to decompose the known ligand set of a target in terms of orthogonal "signals" of salient chemical features, and distinguish these from the much larger set of ligand chemical features that are not relevant for binding to that particular target receptor. After removing the noise caused by finite sampling, we show that the similarity of an unknown ligand to the remaining, cleaned chemical features is a robust predictor of ligand-target affinity, performing as well or better than any algorithm in the published literature. We interpret our algorithm as deriving a model for the binding energy between a target receptor and the set of known ligands, where the underlying binding energy model is related to the classic Ising model in statistical physics.

Understanding the polypharmacological anticancer effects of Xiao Chai Hu Tang via a computational pharmacological model

Xiao Chai Hu Tang (XCHT), a traditional herbal formula, is widely administered as a cancer treatment. However, the underlying molecular mechanisms of its anticancer effects are not fully understood. In the present study, a computational pharmacological model that combined chemical space mapping, molecular docking and network analysis was employed to predict which chemical compounds in XCHT are potential inhibitors of cancer-associated targets, and to establish a compound-target (C-T) network and compound-compound (C-C) association network. The identified compounds from XCHT demonstrated diversity in chemical space. Furthermore, they occupied regions of chemical space that were the same, or close to, those occupied by drug or drug-like compounds that are associated with cancer, according to the Therapeutic Targets Database. The analysis of the molecular docking and the C-T network demonstrated that the potential inhibitors possessed the properties of promiscuous drugs and combination therapies. The C-C network was classified into four clusters and the different clusters contained various multi-compound combinations that acted on different targets. The study indicated that XCHT has a polypharmacological role in treating cancer and the potential inhibitory components of XCHT require further investigation as potential therapeutic strategies for cancer patients.

Computational pharmacological comparison of Salvia miltiorrhiza and Panax notoginseng used in the therapy of cardiovascular diseases

The herb pair comprising Salvia miltiorrhiza (SM) and Panax notoginseng (PN) has been used as a classical formula for cardiovascular diseases (CVDs) in China and in western countries. However, the pharmacology of SM and PN in this herb pair has not been fully elucidated. The aim of this study was to compare the mechanisms of SM and PN at the molecular level for the treatment of CVDs. We used a systems pharmacology approach, integrating ligand clustering, chemical space, docking simulation and network analysis, to investigate these two herbal medicines. The compounds in SM were attached to clusters 2, 3, 5, 6, 8 and 9, while the compounds in PN were attached to clusters 1, 2, 4, 5, 6, 7, 8 and 10. The distributions of chemical space between the compounds from SM and PN were discrete, with the existence of small portions of overlap, and the majority of the compounds did not violate 'Lipinski's rule of five'. Docking indicated that the average number of targets correlated with each compound in SM and PN were 5.0 and 3.6, respectively. The minority nodes in the SM and PN drug-target networks possessed common values of betweenness centrality, closeness centrality, topological coefficients and shortest path length. Furthermore, network analyses revealed that SM and PN exerted different modes of action between compounds and targets. These results suggest that the method of computational pharmacology is able to intuitively trace out the similarities and differences of two herbs and their interaction with targets from the molecular level, and that the combination of two herbs may extend their activities in different potential multidrug combination therapies for CVDs.