Assessment of biological half life using in silico QSPkR approach: A self organizing molecular field analysis (SOMFA) on a series of antimicrobial quinolone drugs

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.

Using computers to ESKAPE the antibiotic resistance crisis

Since the discovery of penicillin, the development and use of antibiotics have promoted safe and effective control of bacterial infections. However, the number of antibiotic-resistance cases has been ever increasing over time. Thus, the drug discovery process demands fast, efficient and cost-effective alternative approaches for developing lead candidates with outstanding performance. Computational approaches are appealing techniques to develop lead candidates in an in silico fashion. In this review, we provide an overview of the implementation of current in silico state-of-the-art techniques, including machine learning (ML) and deep learning (DL), in drug discovery. We also discuss the development of quantum computing and its potential benefits for antibiotics research and current bottlenecks that limit computational drug discovery advancement.

Liquid Chromatographic Enantioseparation of Some Fluoroquinoline Drugs Using Several Polysaccharide-Based Chiral Stationary Phases

The enantioseparation of three fluoroquinoline antibacterial drugs, namely, flumequine, ofloxacin and lomefloxacin using high-performance liquid chromatography was optimized on seven polysaccharide-derived chiral stationary phases, namely, Chiralpak® IB, chiralpak® IA, Chiralpak® AD, Chiralcel® OJ, Chiralcel® OD, Chiralcel® OD-H and Chiralcel® OZ-3 and applying different mobile phases in isocratic mode is described. The role of addition of organic additives was also investigated. A baseline separation of flumequine, ofloxacin and lomefloxacin enantiomers was achieved. Parameters influencing enantioseparation including mobile phase, organic additive and chemical nature of the chiral selector found to be highly influencing on the enantiomeric separation were investigated. Chiral recognition mechanism(s) are also presented.

Molecular docking assisted 3D-QSAR study of benzylidene-2,4-thiazolidinedione derivatives as PTP-1B inhibitors for the management of Type-2 diabetes mellitus

An integrated molecular docking assisted 3D-QSAR study was performed on benzylidene-2,4-thiazolidinediones to identify spatial fingerprints for designing PTP-1B inhibitors.

In silico predicative studies for cytotoxic potential of NSAIDs using self-organizing molecular field analysis

Antiproliferative potential of nonsteroidal anti-inflammatory drugs (NSAIDs) has generated an immense interest among the scientific fraternity to assess its broader role in the chemoprevention of colon cancer. Due to serious adverse events associated with the chemotherapy, NSAIDs have been exploited as adjuvants to synergize the cytotoxic potential of conventional chemotherapeutic agents at low-dose regimens. The present investigation has been focused on in silico model generation for in vitro cytotoxicity activity of the clinically active NSAIDs using self-organizing molecular field analysis (SOMFA) studies. A statistically validated robust model for a diverse group of NSAIDs having flexibility in structure and cytotoxicity activity was obtained using SOMFA. The statistical measures having good cross-validated correlation coefficient r (2) (cv) (.8291), noncross-validated correlation coefficient r (2) values (.8686), and high F test value (41.8722) proved significance in the generated model. Analysis of 3-dimensional quantitative structure activity relationship (3D-QSAR) models through electrostatic and shape grids provided additional valuable information regarding shape and electrostatic potential influence on in vitro cytotoxicity profile. The analysis of SOMFA results provided a better insight about the generation of molecular architecture of novel NSAIDs yet to be synthesized, with optimum in vitro cytotoxicity activity and improved therapeutic profile.

Correlation between in silico physicochemical characteristics of drugs and their mean residence time in human and dog

The correlation between 52 physicochemical parameters and mean residence time (MRT) for 27 drugs used in human and dog were investigated. The physicochemical parameter values calculated provided a basis for deriving a series of arithmetic expressions, which were used to build a mathematical model describing the relationship between them and the MRT values. From the entire set of analyzed parameters, a subset of 14 was identified that contributed to the derivation of an arithmetic expression: Log(PSA - WPSA + ACID) x [XlogP - (LogKp - EAxLn(Caco2 + AMINE + SAF))] + (AMIDE + IP - FG) - Ln(MW + PISA) the value of which is highly correlated with the MRT value in dogs (P < .001) and allowed prediction of the MRT predicted (MRT(pred)). In humans, no correlation was found that allowed the calculation of MRT(pred). These results indicate that predicting the pharmacokinetics of any specific drug for humans based on pharmacokinetic data obtained in the dog should be undertaken with knowledge of the inherent limitations.