Mechanistic inhibition of Monkeypox and Marburg virus infection by O-rhamnosides and Kaempferol-o-rhamnosides derivatives: a new-fangled computational approach

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

Interplay of precision therapeutics and MD study: Calocybe indica's potentials against cervical cancer and its interaction with VEGF via octadecanoic acid

The evolving landscape of personalized medicine necessitates a shift from traditional therapeutic interventions towards precision-driven approaches. Embracing this paradigm, our research probes the therapeutic efficacy of the aqueous crude extract (ACE) of Calocybe indica in cervical cancer treatment, merging botanical insights with advanced molecular research. We observed that ACE exerts significant influences on nuclear morphology and cell cycle modulation, further inducing early apoptosis and showcasing prebiotic attributes. Characterization of ACE have identified several phytochemicals including significant presence of octadeconoic acid. Simultaneously, utilizing advanced Molecular Dynamics (MD) simulations, we deciphered the intricate molecular interactions between Vascular Endothelial Growth Factor (VEGF) and Octadecanoic acid to establish C.indica's role as an anticancer agent. Our study delineates Octadecanoic acid's potential as a robust binding partner for VEGF, with comprehensive analyses from RMSD and RMSF profiles highlighting the stability and adaptability of the protein-ligand interactions. Further in-depth thermodynamic explorations via MM-GBSA calculations reveal the binding landscape of the VEGF-Octadecanoic acid complex. Emerging therapeutic innovations, encompassing proteolysis-targeting chimeras (PROTACs) and avant-garde nanocarriers, are discussed in the context of their synergy with compounds like Calocybe indica P&C. This convergence underscores the profound therapeutic potential awaiting clinical exploration. This study offers a holistic perspective on the promising therapeutic avenues facilitated by C. indica against cervical cancer, intricately woven with advanced molecular interactions and the prospective integration of precision therapeutics in modern oncology.

Molecular dynamics simulations assisted investigation of phytochemicals as potential lead candidates against anti-apoptotic Bcl-B protein

Due to the multifarious nature of cancer, finding a single definitive cure for this dreadful disease remains an elusive challenge. The dysregulation of the apoptotic pathway or programmed cell death, governed by the Bcl-2 family of proteins plays a crucial role in cancer development and progression. Bcl-B stands out as a unique anti-apoptotic protein from the Bcl-2 family that selectively binds to Bax which inhibits its pro-apoptotic function. Although several inhibitors are reported for Bcl-2 family proteins, no specific inhibitors are available against the anti-apoptotic Bcl-B protein. This study aims to address this research gap by using virtual screening of an in-house library of phytochemicals from seven anti-cancer medicinal plants to identify lead molecules against Bcl-B protein. Through pharmacokinetic analysis and molecular docking studies, we identified three lead candidates (Enterolactone, Piperine, and Protopine) based on appreciable drug-likeliness, ADME properties, and binding affinity values. The identified molecules also exhibited specific interactions with critical amino acid residues of the binding cleft, highlighting their potential as lead candidates. Finally, molecular dynamics simulations and MM/PBSA based binding free energy analysis revealed that Enterolactone (CID_114739) and Piperine (CID_638024) molecules were on par with Obatoclax (CID_11404337), which is a known inhibitor of the Bcl-2 family proteins.Communicated by Ramaswamy H. Sarma.

Exploring the efficacy of 1-amino-cyclopropane-1-carboxylic acid (ACCA) as a natural compound in strengthening maize resistance against biotic and abiotic stressors: an empirical computational study

Objective

This study aims to understand plant-bacteria interactions that enhance plant resistance to environmental stressors, with a focus on maize (Zea mays L.) and its vulnerability to various pathogenic organisms. We examine the potential of 1-amino-cyclopropane-1-carboxylic acid (ACCA) as a compound to boost maize's resilience against stressors and pathogens.

Background

With the growing global population and increased food demand, the study of endophytes, comprising bacteria and fungi, becomes crucial. They reside within plant tissues, affecting their hosts either beneficially or detrimentally. Agrobacteria are of specific interest due to their potential to contribute to developing strategies for plant resistance enhancement.

Methods

We conducted exhaustive research on the defense-related proteins and mechanisms involved in maize-pathogen interactions. The efficacy of ACCA as a natural-compound that could enhance maize's resistance was examined.

Results

Our research indicates that ACCA, having a binding energy of -9.98 kcal/mol, successfully strengthens maize resistance against pathogenic assaults and drought stress. It plays a crucial protective role in maize plants as they mature, outperforming other ligands in its effectiveness to improve productivity and increase yield.

Conclusion

Applying ACCA to maize plants has considerable potential in enhancing their resilience and tolerance to stress, proving to be an effective strategy to boost crop yield and productivity. This could help address the increasing global food demand. However, more research is needed to optimize ACCA application methods and to gain a comprehensive understanding of its long-term effects on maize cultivations and the environment.