Rationalizing Underprediction of Drug Clearance from Enzyme and Transporter Kinetic Data: From In Vitro Tools to Mechanistic Modeling

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.

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Drug resistant gut bacteria mimic a host mechanism for anticancer drug clearance

10.1101/820084 ◽

2019 ◽

Author(s):

Peter Spanogiannopoulos ◽

Patrick H. Bradley ◽

Jonathan Melamed ◽

Ysabella Noelle Amora Malig ◽

Kathy N. Lam ◽

...

Keyword(s):

Mammalian Cells ◽

Drug Disposition ◽

Transcriptional Profiling ◽

Drug Efficacy ◽

Drug Clearance ◽

Gut Bacteria ◽

Resistant Bacteria ◽

Drug Resistant ◽

Bacterial Strains

Microbiome surveys indicate that pharmaceuticals are the top predictor of inter-individual variations in gut microbial community structure1, consistent with in vitro evidence that non-antibiotic (i.e. host-targeted) drugs inhibit gut bacterial growth2and are subject to extensive metabolism by the gut microbiome3,4. In oncology, bacterial metabolism has been implicated in both drug efficacy5,6and toxicity7,8; however, the degree to which bacterial sensitivity and metabolism can be driven by conserved pathways also found in mammalian cells remains poorly understood. Here, we show that anticancer fluoropyrimidine drugs broadly inhibit the growth of diverse gut bacterial strains. Media supplementation, transcriptional profiling (RNA-seq), and bacterial genetics implicated pyrimidine metabolism as a key target in bacteria, as in mammalian cells. Drug resistant bacteria metabolized 5FU to its inactive metabolite dihydrofluorouracil (DHFU) mimicking the major host pathway for drug clearance. Functional orthologs of the bacterial operon responsible (preTA) are widespread across human gut bacteria from the Firmicutes and Proteobacteria phyla. The observed conservation of both the targets and pathways for metabolism of therapeutics across domains highlights the need to distinguish the relative contributions of human and microbial cells to drug disposition9, efficacy, and side effect profiles.

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Role of mechanical cues and hypoxia on the growth of tumor cells in strong and weak confinement: A dual in vitro–in silico approach

Science Advances ◽

10.1126/sciadv.aaz7130 ◽

2020 ◽

Vol 6 (13) ◽

pp. eaaz7130 ◽

Cited By ~ 1

Author(s):

V. Le Maout ◽

K. Alessandri ◽

B. Gurchenkov ◽

H. Bertin ◽

P. Nassoy ◽

...

Keyword(s):

In Silico ◽

Time Course ◽

Growth Dynamics ◽

Tumor Model ◽

Mechanistic Modeling ◽

Physical Factors ◽

Factors Affecting ◽

Numerical Studies

Characterization of tumor growth dynamics is of major importance for cancer understanding. By contrast with phenomenological approaches, mechanistic modeling can facilitate disclosing underlying tumor mechanisms and lead to identification of physical factors affecting proliferation and invasive behavior. Current mathematical models are often formulated at the tissue or organ scale with the scope of a direct clinical usefulness. Consequently, these approaches remain empirical and do not allow gaining insight into the tumor properties at the scale of small cell aggregates. Here, experimental and numerical studies of the dynamics of tumor aggregates are performed to propose a physics-based mathematical model as a general framework to investigate tumor microenvironment. The quantitative data extracted from the cellular capsule technology microfluidic experiments allow a thorough quantitative comparison with in silico experiments. This dual approach demonstrates the relative impact of oxygen and external mechanical forces during the time course of tumor model progression.

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Proposal of a Pharmacokinetically Optimized Dosage Regimen of Antibiotics in Patients Receiving Continuous Hemodiafiltration

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.01758-10 ◽

2011 ◽

Vol 55 (12) ◽

pp. 5804-5812 ◽

Cited By ~ 24

Author(s):

Takehito Yamamoto ◽

Nobuhiro Yasuno ◽

Shoichi Katada ◽

Akihiro Hisaka ◽

Norio Hanafusa ◽

...

Keyword(s):

Critically Ill ◽

Critically Ill Patients ◽

Drug Clearance ◽

Minor Effect ◽

Optimal Dosage ◽

Continuous Hemodiafiltration ◽

Unbound Fraction ◽

Artificial Plasma

ABSTRACTThe aim of the study was to quantitatively predict the clearance of three antibiotics, amikacin, vancomycin, and teicoplanin, during continuous hemodiafiltration (CHDF) and to propose their optimal dosage in patients receiving CHDF. For this goal,in vitroCHDF experiments with a polyacrylonitrile (PAN) membrane were first performed using these antibiotics, and then the clearances were compared within vivoCHDF situations determined in 16 critically ill patients. Thein vitroCHDF clearances were described as the product of the outflow rate of a drain (Qoutflow) and the drug unbound fraction in artificial plasma, indicating that drug adsorption to the PAN membrane has minor effect on drug clearance in our settings. The observedin vivoclearances also agreed very well with the predicted values, with a product ofQoutflowand plasma unbound fraction, when residual creatinine clearance (CLCR) was taken into account (within a range of 0.67- to 1.5-fold for 15 of 16 patients). Based on these results, a nomogram of the optimized dosages of amikacin, vancomycin, and teicoplanin was proposed, and it was evident thatQoutflowand residual CLCRare major determinants of the dosage and dosing interval for these antibiotics. Although the applicability needs to be confirmed with another type of membrane or higherQoutflow, our nomogram can help determine the dosage setting in critically ill patients receiving CHDF.

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Combining in Vitro and in Vivo Pharmacokinetic Data for Prediction of Hepatic Drug Clearance in Humans by Artificial Neural Networks and Multivariate Statistical Techniques

Journal of Medicinal Chemistry ◽

10.1021/jm991030j ◽

1999 ◽

Vol 42 (25) ◽

pp. 5072-5076 ◽

Cited By ~ 48

Author(s):

Gisbert Schneider ◽

Philippe Coassolo ◽

Thierry Lavé

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Drug Clearance ◽

Statistical Techniques ◽

Pharmacokinetic Data ◽

Multivariate Statistical Techniques ◽

Multivariate Statistical ◽

Hepatic Drug

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Effect of Albumin on Human Liver Microsomal and Recombinant CYP1A2 Activities: Impact on In Vitro-In Vivo Extrapolation of Drug Clearance

Drug Metabolism and Disposition ◽

10.1124/dmd.111.044057 ◽

2012 ◽

Vol 40 (5) ◽

pp. 982-989 ◽

Cited By ~ 29

Author(s):

Nitsupa Wattanachai ◽

Wichittra Tassaneeyakul ◽

Andrew Rowland ◽

David J. Elliot ◽

Kushari Bowalgaha ◽

...

Keyword(s):

Human Liver ◽

Drug Clearance ◽

In Vivo Extrapolation

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Prediction of cytochrome P450-mediated drug clearance in humans based on the measured activities of selected CYPs

Bioscience Reports ◽

10.1042/bsr20171161 ◽

2017 ◽

Vol 37 (6) ◽

Cited By ~ 7

Author(s):

Jie Gao ◽

Jie Wang ◽

Na Gao ◽

Xin Tian ◽

Jun Zhou ◽

...

Keyword(s):

Cytochrome P450 ◽

Liver Microsomes ◽

Normal Liver ◽

Drug Clearance ◽

Cytochrome P450 Enzymes ◽

Simple Method ◽

Drug Metabolizing Enzyme ◽

Descriptive Models

Determining drug-metabolizing enzyme activities on an individual basis is an important component of personalized medicine, and cytochrome P450 enzymes (CYPs) play a principal role in hepatic drug metabolism. Herein, a simple method for predicting the major CYP-mediated drug clearance in vitro and in vivo is presented. Ten CYP-mediated drug metabolic activities in human liver microsomes (HLMs) from 105 normal liver samples were determined. The descriptive models for predicting the activities of these CYPs in HLMs were developed solely on the basis of the measured activities of a smaller number of more readily assayed CYPs. The descriptive models then were combined with the Conventional Bias Corrected in vitro–in vivo extrapolation method to extrapolate drug clearance in vivo. The Vmax, Km, and CLint of six CYPs (CYP2A6, 2C8, 2D6, 2E1, and 3A4/5) could be predicted by measuring the activities of four CYPs (CYP1A2, 2B6, 2C9, and 2C19) in HLMs. Based on the predicted CLint, the values of CYP2A6-, 2C8-, 2D6-, 2E1-, and 3A4/5-mediated drug clearance in vivo were extrapolated and found that the values for all five drugs were close to the observed clearance in vivo. The percentage of extrapolated values of clearance in vivo which fell within 2-fold of the observed clearance ranged from 75.2% to 98.1%. These findings suggest that measuring the activity of CYP1A2, 2B6, 2C9, and 2C19 allowed us to accurately predict CYP2A6-, 2C8-, 2D6-, 2E1-, and 3A4/5-mediated activities in vitro and in vivo and may possibly be helpful for the assessment of an individual’s drug metabolic profile.

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