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.

Improved antibody pharmacokinetics by disruption of contiguous positive surface potential and charge reduction using alternate human framework

Optimal pharmacokinetic (PK) properties of therapeutic monoclonal antibodies (mAbs) are essential to achieve the desired pharmacological benefits in patients. To accomplish this, we followed an approach comprising structure-based mAb charge engineering in conjunction with the use of relevant preclinical models to screen and select humanized candidates with PK suitable for clinical development. Murine mAb targeting TDP-43, ACI-5891, was humanized on a framework (VH1-3/VK2-30) selected based on the highest sequence homology. Since the initial humanized mAb (ACI-5891.1) presented a fast clearance in non-human primates (NHPs), reiteration of humanization on a less basic human framework (VH1-69-2/VK2-28) while retaining high sequence homology was performed. The resulting humanized variant, ACI-5891.9, presented a six-fold reduction in clearance in NHPs resulting in a significant increase in half-life. The observed reduced clearance of ACI-5891.9 was attributed not only to the overall reduction in isoelectric point (pI) by 2 units, but importantly to a more even surface potential. These data confirm the importance and contribution of surface charges to mAb disposition in vivo. Consistent low clearance of ACI-5891.9 in Tg32 mice, a human FcRn transgenic mouse model, further confirmed its utility for early assessment and prediction of human PK. These data demonstrate that mAb surface charge is an important parameter for consideration during the selection and screening of humanized candidates in addition to maintaining the other key physiochemical and target binding characteristics.

Limoniastrum monopetalum-Mediated Nanoparticles and Biomedicines: In Silico Study and Molecular Prediction of Biomolecules

An in silico approach applying computer-simulated models helps enhance biomedicines by sightseeing the pharmacology of potential therapeutics. Currently, an in silico study combined with in vitro assays investigated the antimicrobial ability of Limoniastrum monopetalum and silver nanoparticles (AgNPs) fabricated by its aid. AgNPs mediated by L. monopetalum were characterized using FTIR, TEM, SEM, and DLS. L. monopetalum metabolites were detected by QTOF-LCMS and assessed using an in silico study for pharmacological properties. The antibacterial ability of an L. monopetalum extract and AgNPs was investigated. PASS Online predictions and the swissADME web server were used for antibacterial activity and potential molecular target metabolites, respectively. Spherical AgNPs with a 68.79 nm average size diameter were obtained. Twelve biomolecules (ferulic acid, trihydroxy-octadecenoic acid, catechin, pinoresinol, gallic acid, myricetin, 6-hydroxyluteolin, 6,7-dihydroxy-5-methoxy 7-O-β-d-glucopyranoside, methyl gallate, isorhamnetin, chlorogenic acid, 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-4-oxo-4H-chromen-3-yl 6-O-(6-deoxy-β-l-mannopyranosyl)-β-d-glucopyranoside) were identified. The L. monopetalum extract and AgNPs displayed antibacterial effects. The computational study suggested that L. Monopetalum metabolites could hold promising antibacterial activity with minimal toxicity and an acceptable pharmaceutical profile. The in silico approach indicated that metabolites 8 and 12 have the highest antibacterial activity, and swissADME web server results suggested the CA II enzyme as a potential molecular target for both metabolites. Novel therapeutic agents could be discovered using in silico molecular target prediction combined with in vitro studies. Among L. Monopetalum metabolites, metabolite 12 could serve as a starting point for potential antibacterial treatment for several human bacterial infections.

Design, Synthesis, SAR, Pharmacokinetic Prediction of New 4-Quinolones as Anti-Microbial Agents

s of new 4-quinolone derivatives was synthesized by conventional heating method. For the synthesized compounds, we performed pharmacokinetic prediction, SAR and antimicrobial assay. The presence of halogen elements plays a key role in the biological activity that is clear by in vitro analysis. Target compounds exhibit moderate to significant activity near to standard marketed drugs like amoxycillin, chloramphenicol, ciprofloxacin, norfloxacin, griseofulvin, and nystatin.