Predicting drug-drug interactions: an FDA perspective

Recent Progress on Physiologically Based Pharmacokinetic (PBPK) Model: A Review Based on Bibliometrics

Physiologically based pharmacokinetic/toxicokinetic (PBPK/PBTK) models are designed to elucidate the mechanism of chemical compound action in organisms based on the physiological, biochemical, anatomical, and thermodynamic properties of organisms. After nearly a century of research and practice, good results have been achieved in the fields of medicine, environmental science, and ecology. However, there is currently a lack of a more systematic review of progress in the main research directions of PBPK models, especially a more comprehensive understanding of the application in aquatic environmental research. In this review, a total of 3974 articles related to PBPK models from 1996 to 24 March 2024 were collected. Then, the main research areas of the PBPK model were categorized based on the keyword co-occurrence maps and cluster maps obtained by CiteSpace. The results showed that research related to medicine is the main application area of PBPK. Four major research directions included in the medical field were "drug assessment", "cross-species prediction", "drug-drug interactions", and "pediatrics and pregnancy drug development", in which "drug assessment" accounted for 55% of the total publication volume. In addition, bibliometric analyses indicated a rapid growth trend in the application in the field of environmental research, especially in predicting the residual levels in organisms and revealing the relationship between internal and external exposure. Despite facing the limitation of insufficient species-specific parameters, the PBPK model is still an effective tool for improving the understanding of chemical-biological effectiveness and will provide a theoretical basis for accurately assessing potential risks to ecosystems and human health. The combination with the quantitative structure-activity relationship model, Bayesian method, and machine learning technology are potential solutions to the previous research gaps.

Long acting tariquidar loaded stearic acid-modified hydroxyapatite enhances brain penetration and antitumor effect of temozolomide

Temozolomide (TMZ) is the first line chemotherapy for glioblastoma (GBM) treatment, but the P-glycoprotein (P-gp) expressed in blood-brain barrier (BBB) will pump out TMZ from the brain leading to decreased TMZ concentration. Tariquidar (TQD), a selective and potent P-gp inhibitor, may be suitable for combination therapy to increase concentration of TMZ in brain. Hydroxyapatite (HAP) is a biodegradable material with sustained release characteristics, and stearic acid surface-modified HAP (SA-HAP) can increase hydrophobicity to facilitate TQD loading. TQD-loaded stearic acid surface-modified HAP (SA-HAP-TQD) was prepared with optimal size and high TQD loading efficiency, and in vitro release and cellular uptake of SA-HAP-TQD showed that SA-HAP-TQD were taken up into lysosome and continuously released TQD from macrophages. In vivo studies have found that over 70 % of SA-HAP was degraded and 80 % of TQD was released from SA-HAP-TQD 28 days after administration. SA-HAP-TQD could increase brain penetration of TMZ, but it would not enhance adverse effects of TMZ in healthy mice. SA-HAP-TQD and TMZ combination had longer median survival than TMZ single therapy in GL261 orthotopic model. These results suggest that SA-HAP-TQD has sustained release characteristics and are potential for improving antitumor effect with TMZ treatment.

VIBRANT: spectral profiling for single-cell drug responses

High-content cell profiling has proven invaluable for single-cell phenotyping in response to chemical perturbations. However, methods with improved throughput, information content and affordability are still needed. We present a new high-content spectral profiling method named vibrational painting (VIBRANT), integrating mid-infrared vibrational imaging, multiplexed vibrational probes and an optimized data analysis pipeline for measuring single-cell drug responses. Three infrared-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. The resulting spectral profile is highly sensitive to phenotypic changes under drug perturbation. Using this property, we built a machine learning classifier to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with new mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic screening.

Physiologically Based Pharmacokinetic (PBPK) Modeling to Predict CYP3A-Mediated Drug Interaction between Saxagliptin and Nicardipine: Bridging Rat-to-Human Extrapolation

The aim of this study was to predict the cytochrome P450 3A (CYP3A)-mediated drug-drug interactions (DDIs) between saxagliptin and nicardipine using a physiologically based pharmacokinetic (PBPK) model. Initially, in silico and in vitro parameters were gathered from experiments or the literature to construct PBPK models for each drug in rats. These models were integrated to predict the DDIs between saxagliptin, metabolized via CYP3A2, and nicardipine, exhibiting CYP3A inhibitory activity. The rat DDI PBPK model was completed by optimizing parameters using experimental rat plasma concentrations after co-administration of both drugs. Following co-administration in Sprague-Dawley rats, saxagliptin plasma concentration significantly increased, resulting in a 2.60-fold rise in AUC, accurately predicted by the rat PBPK model. Subsequently, the workflow of the rat PBPK model was applied to humans, creating a model capable of predicting DDIs between the two drugs in humans. Simulation from the human PBPK model indicated that nicardipine co-administration in humans resulted in a nearly unchanged AUC of saxagliptin, with an approximate 1.05-fold change, indicating no clinically significant changes and revealing a lack of direct translation of animal interaction results to humans. The animal-to-human PBPK model extrapolation used in this study could enhance the reliability of predicting drug interactions in clinical settings where DDI studies are challenging.

Identification and characterization of endogenous biomarkers for hepatic vectorial transport (OATP1B3-P-gp) function using metabolomics with serum pharmacology

The organic anion-transporting polypeptide 1B3 and P-glycoprotein (P-gp) provide efficient directional transport (OATP1B3-P-gp) from the blood to the bile that serves as a key determinant of hepatic disposition of the drug. Unfortunately, there is still a lack of effective means to evaluate the disposal ability mediated by transporters. The present study was designed to identify a suitable endogenous biomarker for the assessment of OATP1B3-P-gp function in the liver. We established stably transfected HEK293T-OATP1B3 and HEK293T-P-gp cell lines. Results showed that azelaic acid (AzA) was an endogenous substrate for OATP1B3 and P-gp using serum pharmacology combined with metabolomics. There is a good correlation between the serum concentration of AzA and probe drugs of rOATP1B3 and rP-gp when rats were treated with their inhibitors. Importantly, after 5-fluorouracil-induced rat liver injury, the relative mRNA level and expression of rOATP1B3 and rP-gp were markedly down-regulated in the liver, and the serum concentration of AzA was significantly increased. These observations suggest that AzA is an endogenous substrate of both OATP1B3 and P-gp, and may serve as a potential endogenous biomarker for the assessment of the function of OATP1B3-P-gp for the prediction of changes in the pharmacokinetics of drugs transported by OATP1B3-P-gp in liver disease states.

Inhibition of Uridine 5'-diphospho-glucuronosyltransferases A10 and B7 by vitamins: insights from in silico and in vitro studies

Uridine 5'-diphospho-glucuronosyltransferases (UGTs) have been considered as a family of enzymes responsible for the glucuronidation process, a crucial phase II detoxification reaction. Among the various UGT isoforms, UGTs A10 and B7 have garnered significant attention due to their broad substrate specificity and involvement in the metabolism of numerous compounds. Recent studies have suggested that certain vitamins may exert inhibitory effects on UGT activity, thereby influencing the metabolism of drugs, environmental toxins, and endogenous substances, ultimately impacting their biological activities. In the present study, the inhibition potential of vitamins (A, B1, B2, B3, B5, B6, B7, B9, D3, E, and C) on UGT1A10 and UGT2B7 was determined using in silico and in vitro approaches. A 3-dimensional model of UGT1A10 and UGT2B7 enzymes was built using Swiss Model, ITASSER, and ROSETTA and verified using Ramachandran plot and SAVES tools. Molecular docking studies revealed that vitamins interact with UGT1A10 and UGT2B7 enzymes by binding within the active site pocket and interacting with residues. Among all vitamins, the highest binding affinity predicted by molecular docking was - 8.61 kcal/mol with vitamin B1. The in vitro studies results demonstrated the inhibition of the glucuronidation activity of UGTs by vitamins A, B1, B2, B6, B9, C, D, and E, with IC50 values of 3.28 ± 1.07 µg/mL, 24.21 ± 1.11 µg/mL, 3.69 ± 1.02 µg/mL, 23.60 ± 1.08 µg/mL, 6.77 ± 1.08 µg/mL, 83.95 ± 1.09 µg/ml, 3.27 ± 1.13 µg/mL and 3.89 ± 1.12 µg/mL, respectively. These studies provided the valuable insights into the mechanisms underlying drug-vitamins interactions and have the potential to guide personalized medicine approaches, optimizing therapeutic outcomes, and ensuring patient safety. Indeed, further research in the area of UGT (UDP-glucuronosyltransferase) inhibition by vitamins is essential to fully understand the clinical relevance and implications of these interactions. UGTs play a crucial role in the metabolism and elimination of various drugs, toxins, and endogenous compounds in the body. Therefore, any factors that can modulate UGT activity, including vitamins, can have implications for drug metabolism, drug-drug interactions, and overall health.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00182-0.

Comprehensive Review of Drug-Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities

Detecting drug-drug interactions (DDIs) is an essential step in drug development and drug administration. Given the shortcomings of current experimental methods, the machine learning (ML) approach has become a reliable alternative, attracting extensive attention from the academic and industrial fields. With the rapid development of computational science and the growing popularity of cross-disciplinary research, a large number of DDI prediction studies based on ML methods have been published in recent years. To give an insight into the current situation and future direction of DDI prediction research, we systemically review these studies from three aspects: (1) the classic DDI databases, mainly including databases of drugs, side effects, and DDI information; (2) commonly used drug attributes, which focus on chemical, biological, and phenotypic attributes for representing drugs; (3) popular ML approaches, such as shallow learning-based, deep learning-based, recommender system-based, and knowledge graph-based methods for DDI detection. For each section, related studies are described, summarized, and compared, respectively. In the end, we conclude the research status of DDI prediction based on ML methods and point out the existing issues, future challenges, potential opportunities, and subsequent research direction.

Drug-drug Interaction between Psychotropic Medications and Medications Used in COVID-19: Comparison of Online Databases

Objective

COVID-19 has gravely affected patients with psychiatric conditions. Potential interactions may occur between psychotropic medications and medications used in treatment of COVID-19. This study aimed to compare the online databases in terms of the quality of drug-drug interaction related information available on them.

Methods

216 drug interactions which included fifty-four psychotropic medication interactions with four COVID-19 drugs across six databases were analyzed by four authors independently. The overall grading of the databases was done on Likert scale independently by the authors using the parameters of ease of understanding for consumers and professionals, level of completeness, discussion on level of evidence and the number of available drugs, congruity with other databases and the mean score was tabulated.

Results

Drugbank and Lexicomp had maximum discrepancy. The safety profile of Hydroxychloroquine was the best (eighteen moderate/severe psychotropic medication reactions) while Ritonavir has worst profile with thirty-nine medications. Drugbank had the highest SCOPE score (1.00) for completeness and covid19druginteractions.com had least (0.81). Overall, Liverpool© Drug Interaction Group and Lexicomp scored the highest (23/30 each) and were the best interaction checker software closely followed by Drugs.com (22/30). Medscape and WebMD were the poorest interaction checker databases.

Conclusion

There is significant variability in the available online databases. Liverpool© Drug Interaction Group and Lexicomp were the most reliable sources for healthcare workers whereas for patients, Drugs.com was the easiest to understand (as it segregates the needs of general consumers and professionals distinctly to explain the interaction).

Assessment of the potential of the MET inhibitor tepotinib to affect the pharmacokinetics of CYP3A4 and P-gp substrates

Tepotinib is a highly selective, potent, mesenchymal-epithelial transition factor (MET) inhibitor, approved for the treatment of non-small cell lung cancer harboring MET exon 14 skipping alterations. The aims of this work were to investigate the potential for drug-drug interactions via cytochrome P450 (CYP) 3A4/5 or P-glycoprotein (P-gp) inhibition. In vitro studies were conducted in human liver microsomes, human hepatocyte cultures and Caco-2 cell monolayers to investigate whether tepotinib or its major metabolite (MSC2571109A) inhibited or induced CYP3A4/5 or inhibited P-gp. Two clinical studies were conducted to investigate the effect of multiple dose tepotinib (500 mg once daily orally) on the single dose pharmacokinetics of a sensitive CYP3A4 substrate (midazolam 7.5 mg orally) and a P-gp substrate (dabigatran etexilate 75 mg orally) in healthy participants. Tepotinib and MSC2571109A showed little evidence of direct or time-dependent CYP3A4/5 inhibition (IC50 > 15 μM) in vitro, although MSC2571109A did show mechanism-based CYP3A4/5 inhibition. Tepotinib did not induce CYP3A4/5 activity in vitro, although both tepotinib and MSC2571109A increased CYP3A4 mRNA. In clinical studies, tepotinib had no effect on the pharmacokinetics of midazolam or its metabolite 1'-hydroxymidazolam. Tepotinib increased dabigatran maximum concentration and area under the curve extrapolated to infinity by 38% and 51%, respectively. These changes were not considered to be clinically relevant. Tepotinib was considered safe and well tolerated in both studies. The potential of tepotinib to cause clinically relevant DDI with CYP3A4- or P-gp-dependent drugs at the clinical dose is considered low. Study 1 (midazolam): NCT03628339 (registered 14 August 2018). Study 2 (dabigatran): NCT03492437 (registered 10 April 2018).

Development of a Rapid LC-MS/MS Method for Simultaneous Quantification of Donepezil and Tadalafil in Rat Plasma: Its Application in a Pharmacokinetic Interaction Study after Oral Administration in Rats

This study aimed to establish a simple and sensitive analytical method to simultaneously quantify donepezil (DPZ) and tadalafil (TAD) in rat plasma using lansoprazole (LPZ) as an internal standard (IS) by using liquid chromatography tandem mass spectrometry. The fragmentation pattern of DPZ, TAD, and IS was elucidated using multiple reaction monitoring in electrospray ionization positive ion mode for the quantification of precursor to production at m/z 380.1 → 91.2 for DPZ, m/z 390.2 → 268.1 for TAD, and m/z 370.3 → 252.0 for LPZ. The extracted DPZ and TAD from plasma using acetonitrile-induced protein precipitation was separated using Kinetex C18 (100 × 2.1 mm, 2.6 µm) column with a gradient mobile phase system consisting of 2 mM ammonium acetate and 0.1% formic acid in acetonitrile at a flow rate of 0.25 mL/min for 4 min. The selectivity, lower limit of quantification, linearity, precision, accuracy, stability, recovery, and matrix effect of this developed method was validated according to the guidelines of the U.S. Food and Drug Administration and the Ministry of Food and Drug Safety of Korea. The established method achieved acceptance criteria in all validation parameters, ensuring reliability, reproducibility, and accuracy, and was successfully implemented in a pharmacokinetic study on the co-administration of DPZ and TAD orally in rats.

Transporters in Regulatory Science: Notable Contributions from Dr. Giacomini in the Past Two Decades

Transporters govern the access of molecules to cells or their exit from cells, thereby controlling the overall distribution of drugs to their intracellular site of action. Clinically relevant drug-drug interactions mediated by transporters are of increasing interest in drug development. Drug transporters, acting alone or in concert with drug metabolizing enzymes, can play an important role in modulating drug absorption, distribution, metabolism, and excretion, thus affecting the pharmacokinetics and/or pharmacodynamics of a drug. Dr. Kathy Giacomini from the University of California, San Francisco is one of the world leaders in transporters and pharmacogenetics with key contributions to transporter science. Her contributions to transporter science are noteworthy. This review paper will summarize Dr. Giacomini's key contributions and influence on transporters in regulatory science in the past two decades. Regulatory science research highlighted in this review covers various aspects of transporter science including understanding the effect of renal impairment on transporters, transporter ontogeny, biomarkers for transporters, and interactions of excipients with transporters affecting drug absorption. Significance Statement This review paper highlights Dr. Giacomini's key contributions and influence on transporters in regulatory science in the past two decades. She has been at the cutting edge of science pertaining to drug transport, drug disposition, and regulatory science, leading to new era of translational sciences pertaining to drug disposition and transporter biology. Her research has and will continue to bring enormous impact on gaining new knowledge in guiding drug development and inspire scientists from all sectors in the field.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

Approaches to minimize the effects of P-glycoprotein in drug transport: A review

P-glycoprotein (P-gp) is a transporter protein that is come under the ATP binding cassette family of proteins. It is situated on the surface of the intestine epithelium, where P-gp substrate binds to the transporter and is pumped into the intestine lumen by the ATP-driven energy-dependent process. In this review, we summarize the role of the P-gp efflux transporter situated on the intestine, the clinical importance of P-gp related drug interactions, and approaches to minimize the effect of P-gp in drug transport. This review also focuses on the impact of P-gp on the bioavailability of the orally administered drug. Many drug's oral bioavailabilities can improve by concomitant use of P-gp inhibitors. Multidrug resistance are reduced by using some naturally occurring compounds obtained from plants and several synthetic P-gp inhibitors. Formulation strategies, one of the most important approaches to mimic the P-gp transporter's action, finally enhancing the oral bioavailability of the drug by inhibiting its P-gp efflux. Vitamin E TPGS, Gelucire 44/14 and other pharmaceutical/formulation excipients inhibit the P-gp efflux. A prodrug approach might be a useful strategy to overcome drug resistance. Prodrug helps to enhance the solubility or alter the pharmacokinetic properties but does not diminish the pharmacological action.

Ceftazidime and cefepime antagonize 5-fluorouracil's effect in colon cancer cells

Background

Drug-drug interaction (DDI), which can occur at the pharmacokinetics and/or the pharmacodynamics (PD) levels, can increase or decrease the therapeutic or adverse response of a drug itself or a combination of drugs. Cancer patients often receive, along their antineoplastic agents, antibiotics such as ß-lactams to treat or prevent infection. Despite the narrow therapeutic indices of antibiotics and antineoplastic agents, data about their potential interaction are insufficient. 5-fluorouracil (5-FU), widely used against colon cancer, is known for its toxicity and large intra- and inter- individual variability. Therefore, knowledge about its interaction with antibiotics is crucial.

Methods

In this study, we evaluated at the PD levels, against HCT-116 colon cancer cells, DDI between 5-FU and several ß-lactams (ampicillin, benzypenicillin, piperacillin, meropenem, flucloxacillin, ceftazidime (CFT), and cefepime (CFP)), widely used in intensive care units. All drugs were tested at clinically achieved concentrations. MTT assay was used to measure the metabolic activity of the cells. Cell cycle profile and apoptosis induction were monitored, in HCT-116 and DLD-1 cells, using propidium iodide staining and Caspase-3/7 activity assay. The uptake of CFT and CFP by the cells was measured using LC-MS/MS method.

Results

Our data indicate that despite their limited uptake by the cells, CFT and CFP (two cephalosporins) antagonized significantly 5-FU-induced S-phase arrest (DLD-1 cells) and apoptosis induction (HCT-116 cells). Remarkably, while CFP did not affect the proliferation of colon cancer cells, CFT inhibited, at clinically relevant concentrations, the proliferation of DLD-1 cells via apoptosis induction, as evidenced by an increase in caspase 3/7 activation. Unexpectedly, 5-FU also antagonized CFT’s induced cell death in DLD-1 cells.

Conclusion

This study shows that CFP and CFT have adverse effects on 5-FU’s action while CFT is a potent anticancer agent that inhibits DLD-1 cells by inducing apoptotic cell death. Further studies are needed to decipher the mechanism(s) responsible for CFT’s effects against colon cancer as well as the observed antagonism between CFT, CFP, and 5-FU with the ultimate aim of translating the findings to the clinical settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09125-4.

A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning

Drug-drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug-drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.

The Effect of Oral Letermovir Administration on the Pharmacokinetics of a Single Oral Dose of P-Glycoprotein Substrate Digoxin in Healthy Volunteers

Letermovir is a human cytomegalovirus (CMV) terminase inhibitor approved in the United States, Canada, Japan, and the European Union for prophylaxis of CMV infection and disease in CMV-seropositive, allogeneic, hematopoietic stem-cell transplant recipients. In vitro, letermovir is a substrate and potential modulator of P-glycoprotein. The potential of letermovir to alter the pharmacokinetics of digoxin (a P-glycoprotein substrate) upon coadministration in healthy subjects was therefore investigated in a phase 1 trial (EudraCT: 2011-004516-39). Oral letermovir 240 mg was administered twice daily for 12 days with a single oral digoxin 0.5-mg dose on day 7; after a washout period, oral digoxin 0.5 mg was administered on day 35 (sequence 1). The period order was reversed after a 28-day washout for sequence 2. Pharmacokinetics and safety were evaluated. The presence of steady-state letermovir reduced digoxin area under the plasma concentration-time curve from administration until last quantifiable measurement by 12% and maximum plasma concentration by 22% compared with digoxin alone; digoxin half-life and elimination rate remained similar in both conditions. The between-subject variability of digoxin maximum plasma concentration was higher with letermovir than without (42% vs 31%) and similar for digoxin area under the plasma concentration-time curve in both periods. No specific safety or tolerability concerns were identified. Overall, letermovir had no clinically relevant effect on concomitant administration with digoxin.

Application of Deep Neural Network Models in Drug Discovery Programs

In silico driven optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety is a key requirement in modern drug discovery. Nowadays, large and harmonized datasets allow to implement deep neural networks (DNNs) as a framework for leveraging predictive models. Nevertheless, various available model architectures differ in their global applicability and performance in lead optimization projects, such as stability over time and interpretability of the results. Here, we describe and compare the value of established DNN-based methods for the prediction of key ADME property trends and biological activity in an industrial drug discovery environment, represented by microsomal lability, CYP3A4 inhibition and factor Xa inhibition. Three architectures are exemplified, our earlier described multilayer perceptron approach (MLP), graph convolutional network-based models (GCN) and a vector representation approach, Mol2Vec. From a statistical perspective, MLP and GCN were found to perform superior over Mol2Vec, when applied to external validation sets. Interestingly, GCN-based predictions are most stable over a longer period in a time series validation study. Apart from those statistical observations, DNN prove of value to guide local SAR. To illustrate this important aspect in pharmaceutical research projects, we discuss challenging applications in medicinal chemistry towards a more realistic picture of artificial intelligence in drug discovery.

Effect of Water Extract of Mangosteen Pericarp on Donepezil Pharmacokinetics in Mice

The pharmacokinetic (PK) change in a drug by co-administered herbal products can alter the efficacy and toxicity. In the circumstances that herb-drug combinations have been increasingly attempted to alleviate Alzheimer's disease (AD), the PK evaluation of herb-drug interaction (HDI) is necessary. The change in systemic exposure as well as target tissue distribution of the drug have been issued in HDIs. Recently, the memory-enhancing effects of water extract of mangosteen pericarp (WMP) has been reported, suggesting a potential for the combination of WMP and donepezil (DNP) for AD treatment. Thus, it was evaluated how WMP affects the PK change of donepezil, including systemic exposure and tissue distribution in mice after simultaneous oral administration of DNP with WMP. Firstly, co-treatment of WMP and donepezil showed a stronger inhibitory effect (by 23.0%) on the neurotoxicity induced by Aβ(25-35) in SH-SY5Y neuroblastoma cells than donepezil alone, suggesting that the combination of WMP and donepezil may be more effective in moderating neurotoxicity than donepezil alone. In PK interaction, WMP increased donepezil concentration in the brain at 4 h (by 63.6%) after administration without affecting systemic exposure of donepezil. Taken together, our results suggest that WMP might be used in combination with DNP as a therapy for AD.

Non-competitive interactions between hydroxychloroquine and azithromycin: Systematic density functional, molecular dynamics, and docking calculations

In this study, density functional theory (DFT) and docking calculations were systematically performed to study the non-competitive interaction between Hydroxychloroquine (HCQ) and azithromycin (AZTH). The calculated changes in Gibbs free energy and enthalpy (at 310 K) were positive, indicating the non-spontaneous formation of HCQ-AZTH specifically in water media. Docking calculation confirmed the obtained DFT result as evident from the different binding sites of both drugs to the SARS-CoV-2 main protease and human angiotensin-converting enzyme 2 (ACE2) proteins. The HCQ-AZTH structure revealed enhanced electrochemical properties, suggesting the synergy between HCQ and AZTH without affecting their therapeutic efficacy against SARS-CoV-2.

Review of unsupervised pretraining strategies for molecules representation

In recent years, the computer-assisted techniques make a great progress in the field of drug discovery. And, yet, the problem of limited labeled data problem is still challenging and also restricts the performance of these techniques in specific tasks, such as molecular property prediction, compound-protein interaction and de novo molecular generation. One effective solution is to utilize the experience and knowledge gained from other tasks to cope with related pursuits. Unsupervised pretraining is promising, due to its capability of leveraging a vast number of unlabeled molecules and acquiring a more informative molecular representation for the downstream tasks. In particular, models trained on large-scale unlabeled molecules can capture generalizable features, and this ability can be employed to improve the performance of specific downstream tasks. Many relevant pretraining works have been recently proposed. Here, we provide an overview of molecular unsupervised pretraining and related applications in drug discovery. Challenges and possible solutions are also summarized.

A prospective whole-mixture approach to assess risk of the food and chemical exposome

Many widely used chemicals result in ubiquitous human exposure from multiple sources, including diet. Legislation mainly deals with the toxicological evaluation of single substances owing to a methodological and conceptual lack of alternatives, and does so within defined silos subject to over 40 distinct regulations in the EU alone. Furthermore, much of the research and many of the initiatives concerned with the assessment and evaluation of chemical mixtures and their potential effects on human health rely on retrospective analysis. Here we propose an approach for the prospective identification, assessment and regulation of mixtures relevant to human health. We address two distinct aspects of toxicology-which chemicals actually do occur together, and how potential mixture-related health hazards can be predicted-with an adapted concept of the exposome and large-scale hazard screens. The proactive use of the likelihood of co-exposure, together with the new approach of methods-based testing, may be a timely and feasible way of identifying those substances and mixtures where hazards may have been overlooked and regulatory action is needed. Ideally, we would generate co-exposure patterns for specific consumer groups, depending on lifestyle and dietary habits, to assess the specific risk of identified mixtures.

Messenger RNA Expression of Albumin, Transferrin, Transthyretin, Asialoglycoprotein Receptor, Cytochrome P450 Isoform, Uptake Transporter, and Efflux Transporter Genes as a Function of Culture Duration in Prolonged Cultured Cryopreserved Human Hepatocytes as Collagen-Matrigel Sandwich Cultures: Evidence for Redifferentiation upon Prolonged Culturing

Hepatic gene expression as a function of culture duration was evaluated in prolonged cultured human hepatocytes. Human hepatocytes from seven donors were maintained as near-confluent collagen-Matrigelsandwich cultures, with messenger RNA expression for genes responsible for key hepatic functions quantified by real-time polymerase chain reaction at culture durations of 0 (day of plating), 2, 7, 9, 16, 23, 26, 29, 36, and 43 days. Key hepatocyte genes were evaluated, including the differentiation markers albumin, transferrin, and transthyretin; the hepatocyte-specific asialoglycoprotein receptor 1 cytochrome P450 isoforms CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4, and CYP3A7; uptake transporter isoforms SLC10A1, SLC22A1, SLC22A7, SLCO1B1, SLCO1B3, and SLCO2B1; efflux transporter isoforms ATP binding cassette (ABC)B1, ABCB11, ABCC2, ABCC3, ABCC4, and ABCG2; and the nonspecific housekeeping gene hypoxanthine ribosyl transferase 1 (HPRT1). The well established dedifferentiation phenomenon was observed on day 2, with substantial (>80%) decreases in gene expression in day 2 cultures observed for all genes evaluated except HPRT1 and efflux transporters ABCB1, ABCC2, ABCC3 (<50% decrease in expression), ABCC4 (>400% increase in expression), and ABCG2 (no decrease in expression). All genes with a >80% decrease in expression were found to have increased levels of expression on day 7, with peak expression observed on either day 7 or day 9, followed by a gradual decrease in expression up to the longest duration evaluated of 43 days. Our results provide evidence that cultured human hepatocytes undergo redifferentiation upon prolonged culturing. SIGNIFICANCE STATEMENT: This study reports that although human hepatocytes underwent dedifferentiation upon 2 days of culture, prolonged culturing resulted in redifferentiation based on gene expression of differentiation markers, uptake and efflux transporters, and cytochrome P450 isoforms. The observed redifferentiation suggests that prolonged (>7 days) culturing of human hepatocyte cultures may represent an experimental approach to overcome the initial dedifferentiation process, resulting in "stabilized" hepatocytes that can be applied toward the evaluation of drug properties requiring an extended period of treatment and evaluation.

Influence of vitamin D treatment on functional expression of drug disposition pathways in human kidney proximal tubule cells during simulated uremia

Effects of cholecalciferol (VitD₃) and calcitriol (1,25-VitD₃), on the expression and function of major vitamin D metabolizing enzymes (cytochrome P450 [CYP]2R1, CYP24A1) and select drug transport pathways (ABCB1/P-gp, SLCO4C1/OATP4C1) were evaluated in human kidney proximal tubule epithelial cells (hPTECs) under normal and uraemic serum conditions.hPTECs were incubated with 10% normal or uraemic serum for 24 h followed by treatment with 2% ethanol vehicle, or 100 and 240 nM doses of VitD₃, or 1,25-VitD₃ for 6 days. The effects of treatment on mRNA and protein expression and functional activity of select CYP enzymes and transporters were assessedUnder uraemic serum, treatment with 1,25-VitD₃ resulted in increased mRNA but decreased protein expression of CYP2R1. Activity of CYP2R1 was not influenced by serum or VitD analogues. CYP24A1 expression was increased with 1,25-VitD₃ under normal as well as uraemic serum, although to a lesser extent. ABCB1/P-gp mRNA expression increased under normal and uraemic serum, with exposure to 1,25-VitD₃. SLCO4C1/OATP4C1 exhibited increased mRNA but decreased protein expression, under uraemic serum + 1,25-VitD₃. Functional assessments of transport showed no changes regardless of exposure to serum or 1,25-VitD₃.Key findings indicate that uraemic serum and VitD treatment led to differential effects on the functional expression of CYPs and transporters in hPTECs.

Biological network analysis with deep learning

Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying algorithms of GNNs. We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein-protein interaction prediction and in silico drug discovery and development. Finally, we highlight application areas such as gene regulatory networks and disease diagnosis where deep learning is emerging as a new tool to answer classic questions like gene interaction prediction and automatic disease prediction from data.

Biological network analysis with deep learning

Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying algorithms of GNNs. We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein-protein interaction prediction and in silico drug discovery and development. Finally, we highlight application areas such as gene regulatory networks and disease diagnosis where deep learning is emerging as a new tool to answer classic questions like gene interaction prediction and automatic disease prediction from data.

Clinical Investigation of Metabolic and Renal Clearance Pathways Contributing to the Elimination of Fevipiprant Using Probenecid as Perpetrator

Fevipiprant, an oral, nonsteroidal, highly selective, reversible, and competitive prostaglandin D₂ receptor 2 antagonist, is eliminated by glucuronidation and by direct renal excretion predominantly via organic anion transporter (OAT) 3. This study aimed to assess the effect of simultaneous UDP-glucuronosyltransferase (UGT) and OAT3 inhibition by probenecid on the pharmacokinetics of fevipiprant and its acyl glucuronide (AG) metabolite to support the dosing recommendation of fevipiprant in the presence of drugs inhibiting these pathways; however, phase III clinical trial results did not support its submission. This was a single-center, open-label, single-sequence, two-period crossover study in healthy subjects. Liquid chromatography with tandem mass spectrometry was used to measure concentrations of fevipiprant and its AG metabolite in plasma and urine. In the presence of probenecid, the mean maximum concentrations of fevipiprant increased approximately 1.7-fold, and the area under the concentration-time curve in plasma increased approximately 2.5-fold, whereas the mean apparent volume of distribution and the AG metabolite:fevipiprant ratio decreased. The apparent systemic clearance decreased by approximately 60% and the renal clearance decreased by approximately 88% in the presence of probenecid. Using these data and those from previous studies, the relative contribution of OAT and UGT inhibition to the overall effect of probenecid was estimated. Furthermore, a general disposition scheme for fevipiprant was developed, showing how a perpetrator drug such as probenecid, which interferes with two key elimination pathways of fevipiprant, causes only a moderate increase in exposure and allows estimation of the drug-drug inhibition when only one of the two pathways is inhibited. SIGNIFICANCE STATEMENT: In this drug-drug interaction (DDI) study, probenecid was used as a tool to inhibit both glucuronidation and active renal secretion of fevipiprant. The combination of plasma and urine pharmacokinetic data from this study with available data allowed the development of a quantitative scheme to describe the fate of fevipiprant in the body, illustrating why the DDI effect on fevipiprant is weak-to-moderate even if a perpetrator drug inhibits several elimination pathways.

Considerations for interactions of drugs used for the treatment of COVID-19 with anti-cancer treatments

SARS-CoV2 infection is an emerging issue worldwide. Cancer patient are at increased risk of infection compared to general population. On the other hand, these patients are at major risk of drug interactions caused by renal and hepatic impairment background. Because of the long-term use of chemotherapy drugs, drug interactions are important in these patients especially with SARS-CoV2 treatments now. This paper is review of reported drug interactions of current treatments for COVID-19 and anticancer agents.

Natural Products as Modulators of CES1 Activity

Carboxylesterase (CES) 1 is the predominant esterase expressed in the human liver and is capable of catalyzing the hydrolysis of a wide range of therapeutic agents, toxins, and endogenous compounds. Accumulating studies have demonstrated associations between the expression and activity of CES1 and the pharmacokinetics and/or pharmacodynamics of CES1 substrate medications (e.g., methylphenidate, clopidogrel, oseltamivir). Therefore, any perturbation of CES1 by coingested xenobiotics could potentially compromise treatment. Natural products are known to alter drug disposition by modulating cytochrome P450 and UDP-glucuronosyltransferase enzymes, but this issue is less thoroughly explored with CES1. We report the results of a systematic literature search and discuss natural products as potential modulators of CES1 activity. The majority of research reports reviewed were in vitro investigations that require further confirmation through clinical study. Cannabis products (Δ ⁹-tetrahydrocannabinol, cannabidiol, cannabinol); supplements from various plant sources containing naringenin, quercetin, luteolin, oleanolic acid, and asiatic acid; and certain traditional medicines (danshen and zhizhuwan) appear to pose the highest inhibition potential. In addition, ursolic acid, gambogic acid, and glycyrrhetic acid, if delivered intravenously, may attain high enough systemic concentrations to significantly inhibit CES1. The provision of a translational interpretation of in vitro assessments of natural product actions and interactions is limited by the dearth of basic pharmacokinetic data of the natural compounds exhibiting potent in vitro influences on CES1 activity. This is a major impediment to assigning even potential clinical significance. The modulatory effects on CES1 expression after chronic exposure to natural products warrants further investigation. SIGNIFICANCE STATEMENT: Modulation of CES1 activity by natural products may alter the course of treatment and clinical outcome. In this review, we have summarized the natural products that can potentially interact with CES1 substrate medications. We have also noted the limitations of existing reports and outlined challenges and future directions in this field.

Ser100-Phosphorylated RORα Orchestrates CAR and HNF4α to Form Active Chromatin Complex in Response to Phenobarbital to Regulate Induction of CYP2B6

We have previously shown that the retinoid-related orphan receptor alpha (RORα) phosphorylation plays a pivotal role in sulfotransferase 1E1 gene regulation within mouse liver. Here, we found serine 100-phosphorylated RORα orchestrates constitutive androstane receptor (CAR) and hepatocyte nuclear factor 4 alpha (HNF4α) to induce CYP2B6 by phenobarbital (PB) in human primary hepatocytes (HPHs). RORα knockdown using small interfering RNAs suppressed CYP2B6 mRNAs in HPH, whereas transient expression of RORα in COS-1 cells activated CYP2B6 promoter activity in reporter assays. Through chromatin immunoprecipitation (IP) and gel shift assays, we found that RORα in the form of phosphorylated (p-) S100 directly bound to a newly identified RORα response element (RORα response element on CYP2B6 promoter, -660/-649) within the CYP2B6 promoter in untreated or treated HPH. In PB-treated HPH, p-Ser100 RORα was both enriched in the distal phenobarbital response element module (PBREM) and the proximal okadaic acid response element (OARE), a known HNF4α binding site. Chromatin conformation capture assay revealed direct contact between the PBREM and OARE only in PB-treated HPH. Moreover, CAR preferably interacted with phosphomimetically mutated RORα at Ser100 residue in co-IP assay. A gel shift assay with a radiolabeled OARE module and nuclear extracts prepared from PB-treated mouse liver confirmed that HNF4α formed a complex with Ser 100-phosphorylated RORα, as shown by supershifted complexes with anti-p-Ser100 RORα and anti-HNF4α antibodies. Altogether, the results established that p-Ser100 RORα bridging the PBREM and OARE orchestrates CAR and HNF4α to form active chromatin complex during PB-induced CYP2B6 expression in human primary hepatocytes. SIGNIFICANCE STATEMENT: CYP2B6 is a vital enzyme for the metabolic elimination of xenobiotics, and it is prone to induction by xenobiotics, including phenobarbital via constitutive androstane receptor (CAR) and hepatocyte nuclear factor 4 alpha (HNF4α). Here, we show that retinoid-related orphan receptor alpha (RORα), through phosphorylated S100 residue, orchestrated CAR-HNF4α interaction on the CYP2B6 promoter in human primary hepatocyte cultures. These results signify not only the role of RORα in the molecular process of CYP2B6 induction, but it also reveals the importance of conserved phosphorylation sites within the DNA-binding domain of the receptor.

Adverse pharmacokinetic interactions between illicit substances and clinical drugs

Adverse pharmacokinetic interactions between illicit substances and clinical drugs are of a significant health concern. Illicit substances are taken by healthy individuals as well as by patients with medical conditions such as mental illnesses, acquired immunodeficiency syndrome, diabetes mellitus and cancer. Many individuals that use illicit substances simultaneously take clinical drugs meant for targeted treatment. This concomitant usage can lead to life-threatening pharmacokinetic interactions between illicit substances and clinical drugs. Optimal levels and activity of drug-metabolizing enzymes and drug-transporters are crucial for metabolism and disposition of illicit substances as well as clinical drugs. However, both illicit substances and clinical drugs can induce changes in the expression and/or activity of drug-metabolizing enzymes and drug-transporters. Consequently, with concomitant usage, illicit substances can adversely influence the therapeutic outcome of coadministered clinical drugs. Likewise, clinical drugs can adversely affect the response of coadministered illicit substances. While the interactions between illicit substances and clinical drugs pose a tremendous health and financial burden, they lack a similar level of attention as drug-drug, food-drug, supplement-drug, herb-drug, disease-drug, or other substance-drug interactions such as alcohol-drug and tobacco-drug interactions. This review highlights the clinical pharmacokinetic interactions between clinical drugs and commonly used illicit substances such as cannabis, cocaine and 3, 4-Methylenedioxymethamphetamine (MDMA). Rigorous efforts are warranted to further understand the underlying mechanisms responsible for these clinical pharmacokinetic interactions. It is also critical to extend the awareness of the life-threatening adverse interactions to both health care professionals and patients.

Evaluation of potential herbal-drug interactions of a standardized propolis extract (EPP-AF®) using an in vivo cocktail approach

ETHNOPHARMACOLOGICAL RELEVANCE

Propolis has been employed extensively in many cultures since ancient times as antiseptic, wound healing, anti-pyretic and others due to its biological and pharmacological properties, such as immunomodulatory, antitumor, anti-inflammatory, antioxidant, antibacterial, antiviral, antifungal, antiparasite activities. But despite its broad and traditional use, there is little knowledge about its potential interaction with prescription drugs.

AIM OF THE STUDY

The main objective of this work was to study the potential herbal-drug interactions (HDIs) of EPP-AF® using an in vivo assay with a cocktail approach.

MATERIALS AND METHODS

Subtherapeutic doses of caffeine, losartan, omeprazole, metoprolol, midazolam and fexofenadine were used. Sixteen healthy adult volunteers were investigated before and after exposure to orally administered 125 mg/8 h (375 mg/day) EPP-AF® for 15 days. Pharmacokinetic parameters were calculated based on plasma concentration versus time (AUC) curves.

RESULTS

After exposure to EPP-AF®, it was observed decrease in the AUC^0-∞ of fexofenadine, caffeine and losartan of approximately 18% (62.20 × 51.00 h.ng/mL), 8% (1085 × 999 h.ng/mL) and 13% (9.01 × 7.86 h.ng/mL), respectively, with all 90% CIs within the equivalence range of 0.80-1.25. On the other hand, omeprazole and midazolam exhibited an increase in AUC^0-∞ of, respectively, approximately 18% (18.90 × 22.30 h.ng/mL) and 14% (1.25 × 1.43 h.ng/mL), with the upper bounds of 90% CIs slightly above 1.25. Changes in pharmacokinetics of metoprolol or its metabolite α-hydroxymetoprolol were not statistically significant and their 90% CIs were within the equivalence range of 0.80-1.25.

CONCLUSIONS

In conclusion, our study shows that EPP-AF® does not clinically change CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A activities, once, despite statistical significant, the magnitude of the changes in AUC values after EPP-AF® were all below 20% and therefore may be considered safe regarding potential interactions involving these enzymes. Besides, to the best of our knowledge this is the first study to assess potential HDIs with propolis.

Deep learning for drug–drug interaction extraction from the literature: a review

Drug–drug interactions (DDIs) are crucial for drug research and pharmacovigilance. These interactions may cause adverse drug effects that threaten public health and patient safety. Therefore, the DDIs extraction from biomedical literature has been widely studied and emphasized in modern biomedical research. The previous rules-based and machine learning approaches rely on tedious feature engineering, which is labourious, time-consuming and unsatisfactory. With the development of deep learning technologies, this problem is alleviated by learning feature representations automatically. Here, we review the recent deep learning methods that have been applied to the extraction of DDIs from biomedical literature. We describe each method briefly and compare its performance in the DDI corpus systematically. Next, we summarize the advantages and disadvantages of these deep learning models for this task. Furthermore, we discuss some challenges and future perspectives of DDI extraction via deep learning methods. This review aims to serve as a useful guide for interested researchers to further advance bioinformatics algorithms for DDIs extraction from the literature.

Design, Synthesis, and Pharmacological Evaluation of Potent Positive Allosteric Modulators of the Glucagon-like Peptide-1 Receptor (GLP-1R)

The therapeutic success of peptidic GLP-1 receptor agonists for treatment of type 2 diabetes mellitus (T2DM) motivated our search for orally bioavailable small molecules that can activate the GLP-1 receptor (GLP-1R) as a well-validated target for T2DM. Here, the discovery and characterization of a potent and selective positive allosteric modulator (PAM) for GLP-1R based on a 3,4,5,6-tetrahydro-1H-1,5-epiminoazocino[4,5-b]indole scaffold is reported. Optimization of this series from HTS was supported by a GLP-1R ligand binding model. Biological in vitro testing revealed favorable ADME and pharmacological profiles for the best compound 19. Characterization by in vivo pharmacokinetic and pharmacological studies demonstrated that 19 activates GLP-1R as positive allosteric modulator (PAM) in the presence of the much less active endogenous degradation product GLP1(9-36)NH₂ of the potent endogenous ligand GLP-1(7-36)NH₂. While these data suggest the potential of small molecule GLP-1R PAMs for T2DM treatment, further optimization is still required towards a clinical candidate.

Simultaneous determination of donepezil, 6-O-desmethyl donepezil and spinosin in beagle dog plasma using liquid chromatography‒tandem mass spectrometry and its application to a drug-drug interaction study

Spinosin, which is traditionally used for sedation and sleep disorders, has recently shown potential effects in alleviating memory loss. As spinosin is the main bioactive component in a standardized dried 50% ethanol extract of the seeds of Zizyphus jujuba var. spinosa, a Phase IIb clinical trial is ongoing, in Korea for the combination of the above extract formulated in a tablet (DHP1401 tablet) with donepezil hydrochloride (Aricept® tablet) in patients with mild to moderate Alzheimer's disease. Therefore, to promote safety and efficacy evaluations, a reliable method for the simultaneous detection and analysis of the two drugs is needed. Toward this end, in this study, we established and validated a rapid and sensitive LC-MS/MS method for the simultaneous determination of donepezil, its pharmacologically active metabolite 6-O-desmethyl donepezil, and spinosin in beagle dog plasma (50 μL). After optimization of the system, we used methanol for simple protein precipitation. Chromatographic separation was performed using a Phenomenex Luna C₁₈ column (100 × 2.0 mm, 3 μm) with a mobile phase consisting of 0.1% formic acid in acetonitrile-0.1% formic acid in distilled water (2:8, v/v) at a flow rate of 0.65 mL/min. All analytes were detected and quantified in selected reaction monitoring mode. All calibration curves showed good linearity (r ≥ 0.9965) over the concentration range of 0.02-20, 0.02-10, and 0.5-250 ng/mL for donepezil, for 6-O-desmethyl donepezil, and spinosin, respectively. This validated method was then successfully applied to a pharmacokinetic study in beagle dogs with no evidence for potential drug-drug interactions between DHP1401 and donepezil hydrochloride. This information and optimized assay can be useful for the anticipated co-administration of these two drugs in clinical settings.

Organic Anion-Transporting Polypeptide Genes Are Not Induced by the Pregnane X Receptor Activator Rifampin: Studies in Hepatocytes In Vitro and in Monkeys In Vivo

Induction potentials of the pregnane X receptor (PXR) activator rifampin (RIF) on transporter genes [e.g., organic anion-transporting polypeptides (OATPs)] are still in its infancy or remain controversial in the field. The present investigations characterized changes in transporter gene expression by RIF in sandwich-cultured hepatocytes from multiple donors of human and cynomolgus monkey using real-time quantitative reverse transcription polymerase chain reaction method. Three-day treatment of RIF significantly induced CYP3A4 (∼60-fold induction), but not CYP1A2 and CYP2D6 genes. SLC51B was the most highly induced uptake transporter gene (>10-fold) in both human and monkey hepatocytes. A greater induction of CYP2C9 was observed in monkey hepatocytes than that in humans. ATP-binding cassette (ABC)B1 and ABCC2 were induced slightly above 2-fold in human and monkey hepatocytes and appeared to be dose-dependent. The induction of OATP and other transporter genes was generally less than 2-fold and considered not clinically relevant. SLCO2B1 was not detectable in monkey hepatocytes. To investigate in vivo OATP induction, RIF (18 mg/kg per day) was orally dosed to cynomolgus monkeys for 7 days. Pitavastatin and antipyrine were intravenously dosed before and after RIF treatment as exogenous probes of OATP and CYP activities, respectively. Plasma coproporphyrin-I (CP-I) and coproporphyrin-III (CP-III) were measured as OATP endogenous biomarkers. Although a significant increase of antipyrine clearance (CL) was observed after RIF treatment, the plasma exposures of pitavastatin, CP-I, and CP-III remained unchanged, suggesting that OATP function was not significantly altered. The results suggested that OATP transporters were not significantly induced by PXR ligand RIF. The data are consistent with current regulatory guidances that the in vitro characterization of transporter induction during drug development is not required. SIGNIFICANCE STATEMENT: Organic anion-transporting polypeptide (OATP) genes were not induced by rifampin in sandwich-cultured human and monkey hepatocytes OATP functions measured by OATP probe pitavastatin and endogenous marker coproporphyrins were not altered in monkeys in vivo by 7-day rifampin treatment. The data suggested that OATP transporters are unlikely induced by the pregnane X receptor ligand rifampin, which are consistent with current regulatory guidances that the in vitro characterization of OATP1B induction during drug development is not required.

Ultra-High-Precision, in-vivo Pharmacokinetic Measurements Highlight the Need for and a Route Toward More Highly Personalized Medicine

Clinical drug dosing would, ideally, be informed by high-precision, patient-specific data on drug metabolism. The direct determination of patient-specific drug pharmacokinetics ("peaks and troughs"), however, currently relies on cumbersome, laboratory-based approaches that require hours to days to return pharmacokinetic estimates based on only one or two plasma drug measurements. In response clinicians often base dosing on age, body mass, pharmacogenetic markers, or other indirect estimators of pharmacokinetics despite the relatively low accuracy of these approaches. Here, in contrast, we explore the use of indwelling electrochemical aptamer-based (E-AB) sensors as a means of measuring pharmacokinetics rapidly and with high precision using a rat animal model. Specifically, measuring the disposition kinetics of the drug tobramycin in Sprague-Dawley rats we demonstrate the seconds resolved, real-time measurement of plasma drug levels accompanied by measurement validation via HPLC-MS on ex vivo samples. The resultant data illustrate the significant pharmacokinetic variability of this drug even when dosing is adjusted using body weight or body surface area, two widely used pharmacokinetic predictors for this important class of antibiotics, highlighting the need for improved methods of determining its pharmacokinetics.

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

Transcriptomic profiling identifies novel mechanisms of transcriptional regulation of the cytochrome P450 (Cyp)3a11 gene

Cytochrome P450 (CYP)3A is the most abundant CYP enzyme in the human liver, and a functional impairment of this enzyme leads to unanticipated adverse reactions and therapeutic failures; these reactions result in the early termination of drug development or the withdrawal of drugs from the market. The transcriptional regulation mechanism of the Cyp3a gene is not fully understood and requires a thorough investigation. We mapped the transcriptome of the Cyp3a gene in a mouse model. The Cyp3a gene was induced using the mPXR activator pregnenolone-16alpha-carbonitrile (PCN) and was subsequently downregulated using lipopolysaccharide (LPS). Our objective was to identify the transcription factors (TFs), epigenetic modulators and molecular pathways that are enriched or repressed by PCN and LPS based on a gene set enrichment analysis. Our analysis shows that 113 genes were significantly upregulated (by at least 1.5-fold) with PCN treatment, and that 834 genes were significantly downregulated (by at least 1.5-fold) with LPS treatment. Additionally, the targets of the 536 transcription factors were enriched by a combined treatment of PCN and LPS, and among these, 285 were found to have binding sites on Cyp3a11. Moreover, the repressed targets of the epigenetic markers HDAC1, HDAC3 and EZH2 were further suppressed by LPS treatment and were enhanced by PCN treatment. By identifying and contrasting the transcriptional regulators that are altered by PCN and LPS, our study provides novel insights into the transcriptional regulation of CYP3A in the liver.

Quantitative prediction of hepatic CYP3A activity using endogenous markers in healthy subjects after administration of CYP3A inhibitors or inducers

Accurate prediction of cytochrome P450 (CYP) 3A activity in the early stage of drug development and in clinical practice is important. This study aimed to evaluate the previously constructed CYP3A activity prediction model after administration of CYP3A inhibitors and inducers and to modify the model for better prediction of CYP3A activity. Healthy male subjects received the following study drugs during three study periods: midazolam alone (control phase); midazolam with 200 mg of itraconazole (CYP3A inhibition phase); and midazolam with 150 mg of rifampicin (CYP3A induction phase). We quantified the concentrations of several endogenous CYP3A markers in both urine and plasma using gas chromatography-mass spectrometry. The urinary markers, including 6β-hydroxy (OH)-cortisol/cortisol, 6β-OH-cortisone/cortisone, 16α-OH-dehydroepiandrosterone (DHEA)/DHEA, 16α-OH-androstenedione (A-dione)/A-dione and 7β-OH-DHEA/DHEA, were significantly correlated with midazolam clearance in both the CYP3A inhibition and induction phases. We constructed a statistical prediction model after integrating data from a previous study to predict midazolam clearance as follows: Ln(midazolam clearance) = 2.5545 + 0.3988 × ln(7β-OH-DHEA/DHEA) + 0.1984 × ln(16α-OH-DHEA/DHEA) + 0.5031 × ln(6β-OH-cortisol/cortisol) - 0.1261 [ln(7β-OH-DHEA/DHEA) × ln(6β-OH-cortisol/cortisol)] (r² = 0.75). We suggest that quantitating endogenous markers in vivo coupled with the statistical prediction model may be useful for predicting CYP3A parameters.

Safe and Appropriate Use of Methadone in Hospice and Palliative Care: Expert Consensus White Paper

Methadone has several unique characteristics that make it an attractive option for pain relief in serious illness, but the safety of methadone has been called into question after reports of a disproportionate increase in opioid-induced deaths in recent years. The American Pain Society, College on Problems of Drug Dependence, and the Heart Rhythm Society collaborated to issue guidelines on best practices to maximize methadone safety and efficacy, but guidelines for the end-of-life scenario have not yet been developed. A panel of 15 interprofessional hospice and palliative care experts from the U.S. and Canada convened in February 2015 to evaluate the American Pain Society methadone recommendations for applicability in the hospice and palliative care setting. The goal was to develop guidelines for safe and effective management of methadone therapy in hospice and palliative care. This article represents the consensus opinion of the hospice and palliative care experts for methadone use at end of life, including guidance on appropriate candidates for methadone, detail in dosing, titration, and monitoring of patients' response to methadone therapy.

Predicting adverse drug reactions of combined medication from heterogeneous pharmacologic databases

BACKGROUND

Early and accurate identification of potential adverse drug reactions (ADRs) for combined medication is vital for public health. Existing methods either rely on expensive wet-lab experiments or detecting existing associations from related records. Thus, they inevitably suffer under-reporting, delays in reporting, and inability to detect ADRs for new and rare drugs. The current application of machine learning methods is severely impeded by the lack of proper drug representation and credible negative samples. Therefore, a method to represent drugs properly and to select credible negative samples becomes vital in applying machine learning methods to this problem.

RESULTS

In this work, we propose a machine learning method to predict ADRs of combined medication from pharmacologic databases by building up highly-credible negative samples (HCNS-ADR). Specifically, we fuse heterogeneous information from different databases and represent each drug as a multi-dimensional vector according to its chemical substructures, target proteins, substituents, and related pathways first. Then, a drug-pair vector is obtained by appending the vector of one drug to the other. Next, we construct a drug-disease-gene network and devise a scoring method to measure the interaction probability of every drug pair via network analysis. Drug pairs with lower interaction probability are preferentially selected as negative samples. Following that, the validated positive samples and the selected credible negative samples are projected into a lower-dimensional space using the principal component analysis. Finally, a classifier is built for each ADR using its positive and negative samples with reduced dimensions. The performance of the proposed method is evaluated on simulative prediction for 1276 ADRs and 1048 drugs, comparing using four machine learning algorithms and with two baseline approaches. Extensive experiments show that the proposed way to represent drugs characterizes drugs accurately. With highly-credible negative samples selected by HCNS-ADR, the four machine learning algorithms achieve significant performance improvements. HCNS-ADR is also shown to be able to predict both known and novel drug-drug-ADR associations, outperforming two other baseline approaches significantly.

CONCLUSIONS

The results demonstrate that integration of different drug properties to represent drugs are valuable for ADR prediction of combined medication and the selection of highly-credible negative samples can significantly improve the prediction performance.

Dose adjustment in orphan disease populations: the quest to fulfill the requirements of physiologically based pharmacokinetics

INTRODUCTION

While the media is engaged and fascinated by the idea of 'Precision Medicine', the nuances related to 'Precision Dosing' seem to be largely ignored. Assuming the 'right drug' is selected, clinicians still need to decide on the 'right dose' for individuals. Ideally, optimal dosing should be studied in clinical trials; however, many drugs on the market lack evidence-based dosing recommendations, and small groups of patients (orphan disease populations) are dependent on local guidance and clinician experience to determine drug dosage adjustments. Areas Covered: This report explores the current understanding of dosing adjustment in special populations and examines the requirements for developing 'in silico' models for pediatric, elderly and pregnant patients. The report also highlights current use of modeling to provide evidence-based recommendations for drug labeling in the absence of complete clinical trials in orphan disease populations. Expert Opinion: Physiologically based pharmacokinetics (PBPK) is an attractive prospect for determining the best drug dosage adjustments in special populations. However, it is not sufficient for individualized, or even stratified dosing, unless the systems (drug-independent) data required to build robust PBPK models are obtained. Such models are not a substitute for clinical trials, but they are an alternative to undocumented and inconsistent guesswork.

A Novel PBPK Modeling Approach to Assess Cytochrome P450 Mediated Drug-Drug Interaction Potential of the Cytotoxic Prodrug Evofosfamide

Evofosfamide is a cytotoxic small‐molecule prodrug preferentially activated under hypoxic conditions. The cytotoxicity of evofosfamide impacted the generation of in vitro drug‐drug interaction (DDI) data, especially in vitro induction results. Therefore, a novel physiologically based pharmacokinetic (PBPK) approach was used, which involved available in vitro and clinical data of evofosfamide and combined it with induction data from the prototypical cytochrome P450 (CYP)3A inducer rifampicin. The area under the concentration‐time curve (AUC) ratios of midazolam were above 0.80, indicating that induction of CYP3A by evofosfamide administered weekly is unlikely to occur in humans. Moreover, static and PBPK modeling showed no clinically relevant inhibition via CYP2B6, CYP2D6, and CYP3A4. In conclusion, PBPK models were used to supplement in vitro information of a cytotoxic compound. This approach may set a precedent for future studies of cytotoxic drugs, potentially reducing the need for clinical DDI studies and providing more confidence in the clinical use of approved cytotoxic compounds for which DDI information is sparse.

Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media

Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.

Considerations from the Innovation and Quality Induction Working Group in Response to Drug-Drug Interaction Guidances from Regulatory Agencies: Focus on CYP3A4 mRNA In Vitro Response Thresholds, Variability, and Clinical Relevance

The Innovation and Quality Induction Working Group presents an assessment of best practice for data interpretation of in vitro induction, specifically, response thresholds, variability, application of controls, and translation to clinical risk assessment with focus on CYP3A4 mRNA. Single concentration control data and Emax/EC₅₀ data for prototypical CYP3A4 inducers were compiled from many human hepatocyte donors in different laboratories. Clinical CYP3A induction and in vitro data were gathered for 51 compounds, 16 of which were proprietary. A large degree of variability was observed in both the clinical and in vitro induction responses; however, analysis confirmed in vitro data are able to predict clinical induction risk. Following extensive examination of this large data set, the following recommendations are proposed. a) Cytochrome P450 induction should continue to be evaluated in three separate human donors in vitro. b) In light of empirically divergent responses in rifampicin control and most test inducers, normalization of data to percent positive control appears to be of limited benefit. c) With concentration dependence, 2-fold induction is an acceptable threshold for positive identification of in vitro CYP3A4 mRNA induction. d) To reduce the risk of false positives, in the absence of a concentration-dependent response, induction ≥ 2-fold should be observed in more than one donor to classify a compound as an in vitro inducer. e) If qualifying a compound as negative for CYP3A4 mRNA induction, the magnitude of maximal rifampicin response in that donor should be ≥ 10-fold. f) Inclusion of a negative control adds no value beyond that of the vehicle control.

Cloning and Transcriptional Activity Analysis of the Porcine Abcb1 Gene Promoter: Transcription Factor Sp1 Regulates the Expression of Porcine Abcb1

P-Glycoprotein (P-gp, Abcb1) plays a crucial role in drug disposition and functions by hydrolyzing ATP. However, little is known about the regulatory elements governing the transcription of the porcine Abcb1 gene. In this study, the transcription start site of the pig Abcb1 gene was identified by 5'-RACE. A 1.9-kb fragment of the 5'-flanking region of the Abcb1 gene was cloned from pig genomic DNA and sequenced. The region critical for its promoter activity was investigated via progressive deletions. Further, using mutation assays, two proximal Sp1 binding sites within the 5'-flanking region of Abcb1 were proven to be important cis-regulatory elements involved in regulating the constitutive expression of porcine Abcb1. RNA interference experiments showed that Sp1 regulated the expression of the porcine P-gp at both mRNA and protein levels. Hence, the current work provides valuable information on the regulatory mechanisms of pig Abcb1.

Relationships of Changes in Pharmacokinetic Parameters of Substrate Drugs in Drug-Drug Interactions on Metabolizing Enzymes and Transporters

A general objective of drug-drug interaction (DDI) studies is to determine whether potential interactions of new molecular entities with concomitantly administered other drugs exist and, if DDIs occur, whether dosage adjustments are required. A typical end point for DDI evaluations is the ratio of area under the plasma concentration-time curve (AUC) of substrate drugs (AUCR), whereas the ratios of maximal plasma concentration (C_max ) and terminal half-life (t_1/2 ) are also important to understand DDI mechanisms (C_max R and t_1/2 R, respectively). Because changes in substrate AUC by precipitant drugs ultimately result from alterations of C_max and t_1/2 , AUCR can be considered a hybrid parameter of C_max R and t_1/2 R, for example, AUCR ≈ C_max R  ×  t_1/2 R. The primary objective of this study was to investigate the relationships between AUCR, C_max R, and t_1/2 R in physiologically based pharmacokinetic model-predicted and clinically observed DDI results. First, the model-predicted results showed the excellent proportional relationship between AUCR and (C_max R × t_1/2 R) in DDI results of virtual substrates having a wide range of oral bioavailability with coadministration of ketoconazole, ritonavir, and rifampin. Second, the reasonable proportional relationships were also observed in the clinically observed DDI results of midazolam and statins (atorvastatin, cerivastatin, fluvastatin, lovastatin, pitavastatin, pravastatin, rosuvastatin, and simvastatin) with various inhibitors and inducers. Finally, these results suggest that utilization of the proportional relationship between AUCR and (C_max R × t_1/2 R) can provide an additional framework to further interpret DDI results reasonably and clearly. Furthermore, the proportional relationship can be purposely used to assess study design and pharmacokinetic analyses in DDI studies.