In Silico Development of Combinatorial Therapeutic Approaches Targeting Key Signaling Pathways in Metabolic Syndrome.

Purpose: Dysregulations of key signaling pathways in metabolic syndrome are multifactorial, eventually leading to cardiovascular events. Hyperglycemia in conjunction with dyslipidemia induces insulin resistance and provokes release of proinflammatory cytokines resulting in chronic inflammation, accelerated lipid peroxidation with further development of atherosclerotic alterations and diabetes. We have proposed a novel combinatorial approach using FDA approved compounds targeting IL-17a and DPP4 to ameliorate a significant portion of the clustered clinical risks in patients with metabolic syndrome.

A hybrid modeling approach for assessing mechanistic models of small molecule partitioning in vivo using a machine learning-integrated modeling platform.

Prediction of the first-in-human dosing regimens is a critical step in drug development and requires accurate quantitation of drug distribution. Traditional in vivo studies used to characterize clinical candidate's volume of distribution are error-prone, time- and cost-intensive and lack reproducibility in clinical settings. The paper demonstrates how a computational platform integrating machine learning optimization with mechanistic modeling can be used to simulate compound plasma concentration profile and predict tissue-plasma partition coefficients with high accuracy by varying the lipophilicity descriptor logP.

Effects of Magnesium, Calcium, and Aluminum Chelation on Fluoroquinolone Absorption Rate and Bioavailability: A Computational Study.

Fluoroquinolones (FQs) are a widespread class of broad-spectrum antibiotics prescribed as a first line of defense, and, in some cases, as the only treatment against bacterial infection. However, when administered orally, reduced absorption and bioavailability can occur due to chelation in the gastrointestinal tract (GIT) with multivalent metal cations acquired from diet, coadministered compounds (sucralfate, didanosine), or drug formulation. Predicting the extent to which this interaction reduces in vivo antibiotic absorption and systemic exposure remains desirable yet challenging.

Accelerated Repurposing and Drug Development of Pulmonary Hypertension Therapies for COVID-19 Treatment Using an AI-Integrated Biosimulation Platform.

The COVID-19 pandemic has reached over 100 million worldwide. Due to the multi-targeted nature of the virus, it is clear that drugs providing anti-COVID-19 effects need to be developed at an accelerated rate, and a combinatorial approach may stand to be more successful than a single drug therapy. Among several targets and pathways that are under investigation, the renin-angiotensin system (RAS) and specifically angiotensin-converting enzyme (ACE), and Ca-mediated SARS-CoV-2 cellular entry and replication are noteworthy.

In Silico Simulation of the Systemic Drug Exposure Following the Topical Application of Opioid Analgesics in Patients with Cutaneous Lesions.

The use of opioid analgesics in treating severe pain is frequently associated with putative adverse effects in humans. Topical agents that are shown to have high efficacy with a favorable safety profile in clinical settings are great alternatives for pain management of multimodal analgesia. However, the risk of side effects induced by transdermal absorption and systemic exposure is of great concern as they are challenging to predict.

Scalable in silico Simulation of Transdermal Drug Permeability: Application of BIOiSIM Platform.

Introduction: Transdermal drug delivery is gaining popularity as an alternative to traditional routes of administration. It can increase patient compliance because of its painless and noninvasive nature, aid compounds in bypassing presystemic metabolic effects, and reduce the likelihood of adverse effects through decreased systemic exposure. In silico physiological modeling is critical to predicting dermal exposure for a therapeutic and assessing the impact of different formulations on transdermal disposition.