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 Ca2+-mediated SARS-CoV-2 cellular entry and replication are noteworthy. A combination of ACE inhibitors and calcium channel blockers (CCBs), a critical line of therapy for pulmonary hypertension, has shown therapeutic relevance in COVID-19 when investigated independently. To that end, we conducted in silico modeling using BIOiSIM, an AI-integrated mechanistic modeling platform by utilizing known preclinical in vitro and in vivo datasets to accurately simulate systemic therapy disposition and site-of-action penetration of the CCBs and ACEi compounds to tissues implicated in COVID-19 pathogenesis.
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. In this study, we focus on quinolone interactions with magnesium, calcium and aluminum as found in dietary supplements, antacids (Maalox) orally administered therapies (sucralfate, didanosine). The effect of FQ–metal complexation on absorption rate was investigated through a combined molecular and pharmacokinetic (PK) modeling study. Quantum mechanical calculations elucidated FQ–metal binding energies, which were leveraged to predict the magnitude of reduced bioavailability via a quantitative structure–property relationship (QSPR). This work will help inform clinical FQ formulation design, alert to possible dietary effects, and shed light on drug–drug interactions resulting from coadministration at an earlier stage in the drug development pipeline.
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. Methods: The present study aimed at developing a physiologically based transdermal platform, "BIOiSIM", that could be globally applied to a wide variety of compounds to predict their transdermal disposition. The platform integrates a 16-compartment model of compound pharmacokinetics and was used to simulate and predict drug exposure of three chemically and biologically distinct drug-like compounds. Machine learning optimization was composed of two components: exhaustive search algorithm (coarse-tuning) and descent (fine-tuning) integrated with the platform used to quantitatively determine parameters influencing pharmacokinetics (eg permeability, k perm) of test compounds. Results: The model successfully predicted drug exposure (AUC, C max and T max) following transdermal application of morphine, buprenorphine and nicotine in human subjects, mostly with less than twofold absolute average fold error (AAFE). The model was further able to successfully characterize the relationship between observed systemic exposure and intended pharmacological effect. The predicted systemic concentration of morphine and plasma levels of endogenous pain biomarkers were used to estimate the effectiveness of a given therapeutic regimen. Conclusion: BIOiSIM marks a novel approach to in silico prediction that will enable leveraging of machine learning technology in the pharmaceutical space. The approach to model development outlined results in scalable, accurate models and enables the generation of large parameter/coefficient datasets from in vivo clinical data that can be used in future work to train quantitative structure activity relationship (QSAR) models for predicting likelihood of compound utility as a transdermally administered therapeutic.
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. The approach applied to chemically diverse small molecules resulted in comparable geometric mean fold-errors of 1.50 and 1.63 in pharmacokinetic outputs for direct tissue:plasma partition and hybrid logP optimization, with the latter enabling prediction of tissue permeation that can be used to guide toxicity and efficacy dosing in human subjects. The optimization simulations required to achieve these results were parallelized on the AWS cloud and generated outputs in under 5 h. Accuracy, speed, and scalability of the framework indicate that it can be used to assess the relevance of other mechanistic relationships implicated in pharmacokinetic-pharmacodynamic phenomena with a lower risk of overfitting datasets and generate large database of physiologically-relevant drug disposition for further integration with machine learning models.
A World Health Organization-declared pandemic, COVID-19, has affected more than 4 million people worldwide with over 100,000 deaths and growing in the United States. Due to the fast-spreading and multi-targeted nature of the virus, it is clear that drugs and/or vaccines 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 Ca2+ -mediated SARS-CoV-2 cellular entry and replication are noteworthy. A combination of ACE inhibitors (e.g. benazepril) and calcium channel blockers (CCB, e.g. amlodipine), a critical line of therapy for pulmonary hypertension, has shown therapeutic relevance in COVID-19 when investigated independently. To that end, we conducted in silico modeling using BIOiSIM, an AI-integrated mechanistic modeling platform by utilizing known preclinical in vitro and in vivo datasets to accurately simulate systemic therapy disposition and site-of-action penetration of the CCB and ACEI compounds to tissues implicated in COVID-19 pathogenesis.
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