Mitigating Paxlovid™-induced drug‒drug interaction toxicity: an insight.

The clinical drug-drug interaction side-effects of Ritonavir, an ingredient in Paxlovid™, have been documented, highlighting the need to explore alternative administration methods for Nirmatrelvir, another drug in the Paxlovid™ combination. In this study, the skin permeability potential of Nirmatrelvir was assessed using various models. The prediction results suggest that Nirmatrelvir could be administrated via the transdermal route, offering a promising avenue to enhance the efficacy of anti-COVID-19 agents.

Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA.

Topical and transdermal treatments have been dramatically growing recently and it is crucial to consider skin sensitization during the drug discovery and development process for these administration routes. Various tests, including animal and non-animal approaches, have been devised to assess the potential for skin sensitization. Furthermore, numerous in silico models have been created, providing swift and cost-effective alternatives to traditional methods such as in vivo, in vitro, and in chemico methods for categorizing compounds.

In Silico Prediction of Skin Permeability Using a Two-QSAR Approach.

Topical and transdermal drug delivery is an effective, safe, and preferred route of drug administration. As such, skin permeability is one of the critical parameters that should be taken into consideration in the process of drug discovery and development. The ex vivo human skin model is considered as the best surrogate to evaluate in vivo skin permeability.

Prediction of Skin Sensitization: ?

Skin direct contact with chemical or physical substances is predisposed to allergic contact dermatitis (ACD), producing various allergic reactions, namely rash, blister, or itchy, in the contacted skin area. ACD can be triggered by various extremely complicated adverse outcome pathways (AOPs) remains to be causal for biosafety warrant. As such, commercial products such as ointments or cosmetics can fulfill the topically safe requirements in animal and non-animal models including allergy.

Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability.

Drug absorption is one of the critical factors that should be taken into account in the process of drug discovery and development. The human colon carcinoma cell layer (Caco-2) model has been frequently used as a surrogate to preliminarily investigate the intestinal absorption. In this study, a quantitative structure-activity relationship (QSAR) model was generated using the innovative machine learning-based hierarchical support vector regression (HSVR) scheme to depict the exceedingly confounding passive diffusion and transporter-mediated active transport.

In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression.

The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure-activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes.