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.

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.

Driving success in personalized medicine through AI-enabled computational modeling.

The development of successful drugs is expensive and time-consuming because of high clinical attrition rates. This is caused partially by the rupture seen in the translatability of the drug from the bench to the clinic in the context of personalized medicine. Artificial intelligence (AI)-driven platforms integrated with mechanistic modeling have become instrumental in accelerating the drug development process by leveraging data ubiquitously across the various phases.

Molecular Simulation and Statistical Learning Methods toward Predicting Drug-Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design.

Amorphous solid dispersions (ASDs) have emerged as widespread formulations for drug delivery of poorly soluble active pharmaceutical ingredients (APIs). Predicting the API solubility with various carriers in the API-carrier mixture and the principal API-carrier non-bonding interactions are critical factors for rational drug development and formulation decisions. Experimental determination of these interactions, solubility, and dissolution mechanisms is time-consuming, costly, and reliant on trial and error.

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.

Entering the era of computationally driven drug development.

Historically, failure rates in drug development are high; increased sophistication and investment throughout the process has shifted the reasons for attrition, but the overall success rates have remained stubbornly and consistently low. Only 8% of new entities entering clinical testing gain regulatory approval, indicating that significant obstacles still exist for efficient therapeutic development. The continued high failure rate can be partially attributed to the inability to link drug exposure with the magnitude of observed safety and efficacy-related pharmacodynamic (PD) responses; frequently, this is a result of nonclinical models exhibiting poor prediction of human outcomes across a wide range of disease conditions, resulting in faulty evaluation of drug toxicology and efficacy.

Glycosyltransferase ST6GAL1 contributes to the regulation of pluripotency in human pluripotent stem cells.

Many studies have suggested the significance of glycosyltransferase-mediated macromolecule glycosylation in the regulation of pluripotent states in human pluripotent stem cells (hPSCs). Here, we observed that the sialyltransferase ST6GAL1 was preferentially expressed in undifferentiated hPSCs compared to non-pluripotent cells. A lectin which preferentially recognizes α-2,6 sialylated galactosides showed strong binding reactivity with undifferentiated hPSCs and their glycoproteins, and did so to a much lesser extent with differentiated cells.

Microfluidic capture of endothelial colony-forming cells from human adult peripheral blood: phenotypic and functional validation in vivo.

Introduction: Endothelial colony-forming cells (ECFCs) are endothelial progenitors that circulate in peripheral blood and are currently the subject of intensive investigation due to their therapeutic potential. However, in adults, ECFCs comprise a very small subset among circulating cells, which makes their isolation a challenge.

Materials And Methods: Currently, the standard method for ECFC isolation relies on the separation of mononuclear cells and erythrocyte lysis, steps that are time consuming and known to increase cell loss.