Predicting in vivo drug interactions from in vitro drug discovery data

Finding a needle in the haystack: ADME and pharmacokinetics/pharmacodynamics characterization and optimization toward orally available bifunctional protein degraders

INTRODUCTION

Degraders are an increasingly important sub-modality of small molecules as illustrated by an ever-expanding number of publications and clinical candidate molecules in human trials. Nevertheless, their preclinical optimization of ADME and PK/PD properties has remained challenging. Significant research efforts are being directed to elucidate underlying principles and to derive rational optimization strategies.

AREAS COVERED

In this review the authors summarize current best practices in terms of in vitro assays and in vivo experiments. Furthermore, the authors collate and comment on the current understanding of optimal physicochemical characteristics and their impact on absorption, distribution, metabolism and excretion properties including the current knowledge of Drug-Drug interactions. Finally, the authors describe the Pharmacokinetic prediction and Pharmacokinetic/Pharmacodynamic -concepts unique to degraders and how to best implement these in research projects.

EXPERT OPINION

Despite many recent advances in the field, continued research will further our understanding of rational design regarding degrader optimization. Machine-learning and computational approaches will become increasingly important once larger, more robust datasets become available. Furthermore, tissue-targeting approaches (particularly regarding the Central Nervous System will be increasingly studied to elucidate efficacious drug regimens that capitalize on the catalytic mode of action. Finally, additional specialized approaches (e.g. covalent degraders, LOVdegs) can enrich the field further and offer interesting alternative approaches.

Novel (Q)SAR models for prediction of reversible and time-dependent inhibition of cytochrome P450 enzymes

The 2020 FDA drug-drug interaction (DDI) guidance includes a consideration for metabolites with structural alerts for potential mechanism-based inhibition (MBI) and describes how this information may be used to determine whether in vitro studies need to be conducted to evaluate the inhibitory potential of a metabolite on CYP enzymes. To facilitate identification of structural alerts, an extensive literature search was performed and alerts for mechanism-based inhibition of cytochrome P450 enzymes (CYP) were collected. Furthermore, five quantitative structure-activity relationship (QSAR) models were developed to predict not only time-dependent inhibition of CYP3A4, an enzyme that metabolizes approximately 50% of all marketed drugs, but also reversible inhibition of 3A4, 2C9, 2C19 and 2D6. The non-proprietary training database for the QSAR models contains data for 10,129 chemicals harvested from FDA drug approval packages and published literature. The cross-validation performance statistics for the new CYP QSAR models range from 78% to 84% sensitivity and 79%-84% normalized negative predictivity. Additionally, the performance of the newly developed QSAR models was assessed using external validation sets. Overall performance statistics showed up to 75% in sensitivity and up to 80% in normalized negative predictivity. The newly developed models will provide a faster and more effective evaluation of potential drug-drug interaction caused by metabolites.

Deep Learning of CYP450 Binding of Small Molecules by Quantum Information

Drug-drug interaction can lead to diminished therapeutic effects or increased toxicity, posing significant risks, especially in polypharmacy, and cytochrome P450 plays an indispensable role in this interaction. Cytochrome P450, responsible for the metabolism and detoxification of most drugs, metabolizes about 90% of Food and Drug Administration-approved drugs, making early detection of potential drug-drug interactions. Over the years, in-silico modeling has become a valuable tool for predicting drug-drug interactions. Still, conventional molecular descriptors focusing on the structural properties of drugs often overlook complex electronic interactions critical for accurate predictions. To address this, we implemented the Manifold Embedding of Molecular Surface (MEMS) approach, which retains the quantum mechanical characteristics of molecules. MEMS-generated electronic attributes were embedded and featurized for deep learning using the DeepSets architecture, where our models achieved high accuracy, particularly for cytochrome P450 enzyme 1A2 (CYP1A2), with F1 scores reaching up to 0.866. This study highlights the potential of integrating detailed electronic properties with deep learning to improve predictive models for drug-drug interactions, addressing the limitations of traditional molecular descriptors and machine-learning techniques.

Kinetics of the inhibition of CYP3A4 and CYP2C19 activity by jabara juice and identification of the responsible inhibitory components

Some citrus fruits are known to cause clinically significant drug interactions by inhibiting intestinal cytochrome P450 (CYP) enzymes. This in vitro study aimed to investigate the kinetics of the inhibition of CYP3A4 and CYP2C19 by the juice of jabara, a Japanese citrus fruit that does not contain furanocoumarins such as 6',7'-dihydroxybergamottin, and to identify the inhibitory compound(s). CYP3A4 and CYP2C19 activity levels were determined in vitro using recombinant CYP preparations and their respective substrates. The ethyl acetate extract (EAE) of jabara juice was separated to isolate and identify the compound(s) that inhibited CYP3A4. Then, the time-dependent kinetics of the inhibition of CYP3A4 and CYP2C19 by the EAE and its inhibitory compound(s) were analyzed. The EAE of jabara juice was found to inhibit CYP3A4 in a time-dependent manner. Two flavonoids, 3,3',4',5,6,7,8-heptamethoxyflavone (HpMF) and 3,3',4',5,6,7-hexamethoxyflavone (HxMF), were identified as the responsible compounds. HpMF and HxMF inhibited CYP3A4 activity in a concentration- and time-dependent manner, with inhibition constants (KI) of 10.0 and 7.90 µM and maximal inactivation rate constants (kinact,max) of 0.00856 and 0.0134 min-1, respectively. The EAE did not inhibit CYP2C19, even when preincubation was employed. These findings imply that jabara juice may cause food-drug interactions via time-dependent inhibition of intestinal CYP3A4.

Positioning Enzyme- and Transporter-Based Precipitant Drug-Drug Interaction Studies in Drug Design

In vitro assessment of the potential of compounds to affect drug metabolizing enzymes and transporters and perpetrate drug-drug interactions (DDIs) is a common practice in drug research. For the development phase, regulators define an exhaustive list of enzymes and transporters to consider, but DDIs associated with many of these are minor and can be well-managed in the clinic; thus, progression of drug candidates that address unmet medical needs should not be curtailed due to this property. However, some enzymes and transporters are very important in drug disposition, so it is important to avoid/reduce inhibition or induction of these through drug design. Herein, simplified criteria and methodologies amenable to high-throughput screening are defined to enable drug design to address DDI risk. A strategy is proposed that focuses on the most important enzymes and transporters: namely, cytochrome P450 (CYP) 3A4, CYP2C9, and CYP2D6, organic anion transporting polypeptide (OATP) 1B1, and breast cancer resistant protein (BCRP).

Microdose Cocktail Study Reveals the Activity and Key Influencing Factors of OATP1B, P-Gp, BCRP, and CYP3A in End-Stage Renal Disease Patients

OATP1B, P-gp, BCRP, and CYP3A are the most contributing drug-metabolizing enzymes or transporters (DMETs) for commonly prescribed medication. Their activities may change in end-stage renal disease (ESRD) patients with large inter-individual variabilities (IIVs), leading to altered substrate drug exposure and ultimately elevated safety risk. However, the changing extent and indictive influencing factors are not quantified so far. Here, a microdose cocktail regimen containing five sensitive substrate drugs (pitavastatin, dabigatran etexilate, rosuvastatin, midazolam, and atorvastatin) for these DMETs was administrated to Chinese healthy volunteers and ESRD patients. Drug pharmacokinetics profiles were determined, together with physiological, pharmacogenetic, and gut microbiome signature. Population pharmacokinetic and machine learning model were established to identify key influencing factors and quantify their contribution to drug exposure change. The exposure of pitavastatin, dabigatran, rosuvastatin, and atorvastatin increased to 1.8-, 3.1-, 1.1-, and 1.3-fold, respectively, whereas midazolam exposure decreased by 72% in ESRD patients. Notably, in addition to disease state, the relative abundance of genus Veillonella and Clostridium_XIVb were firstly identified as significant influencing factors for PTV and RSV apparent clearance, respectively, suggesting their indicative role for OATP and BCRP activity evaluation. Moreover, several genera were found to strongly associate with drug clearance and reduce unexplained IIVs. Accordingly, it was estimated that OATP1B and intestine P-gp activity decreased by 35-75% and 29-44%, respectively, whereas BCRP and CYP3A4 activity may upregulate to some extent. Our study provides a quantitative and mechanistic understanding of individual DMET activity and could support precision medicine of substrate drugs in ESRD patients.

Functional dynamics of G protein-coupled receptors reveal new routes for drug discovery

G protein-coupled receptors (GPCRs) are the largest human membrane protein family that transduce extracellular signals into cellular responses. They are major pharmacological targets, with approximately 26% of marketed drugs targeting GPCRs, primarily at their orthosteric binding site. Despite their prominence, predicting the pharmacological effects of novel GPCR-targeting drugs remains challenging due to the complex functional dynamics of these receptors. Recent advances in X-ray crystallography, cryo-electron microscopy, spectroscopic techniques and molecular simulations have enhanced our understanding of receptor conformational dynamics and ligand interactions with GPCRs. These developments have revealed novel ligand-binding modes, mechanisms of action and druggable pockets. In this Review, we highlight such aspects for recently discovered small-molecule drugs and drug candidates targeting GPCRs, focusing on three categories: allosteric modulators, biased ligands, and bivalent and bitopic compounds. Although studies so far have largely been retrospective, integrating structural data on ligand-induced receptor functional dynamics into the drug discovery pipeline has the potential to guide the identification of drug candidates with specific abilities to modulate GPCR interactions with intracellular effector proteins such as G proteins and β-arrestins, enabling more tailored selectivity and efficacy profiles.

Utility of physiologically based pharmacokinetic modeling in predicting and characterizing clinical drug interactions

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic dynamic modeling approach that can be used to predict or retrospectively describe changes in drug exposure due to drug-drug interactions (DDIs). With advancements in commercially available PBPK software, PBPK DDI modeling has become a mainstream approach from early drug discovery through to late-stage drug development and is often used to support regulatory packages for new drug applications. This Minireview will briefly describe the approaches to predicting DDI using PBPK and static modeling approaches, the basic model structures and features inherent to PBPK DDI models, and key examples where PBPK DDI models have been used to describe complex DDI mechanisms. Future directions aimed at using PBPK models to characterize transporter-mediated DDI, predict DDI in special populations, and assess the DDI potential of protein therapeutics will be discussed. A summary of the 209 PBPK DDI examples published to date in 2023 will be provided. Overall, current data and trends suggest a continued role for PBPK models in the characterization and prediction of DDI for therapeutic molecules. SIGNIFICANCE STATEMENT: Physiologically based pharmacokinetic (PBPK) models have been a key tool in the characterization of various pharmacokinetic phenomena, including drug-drug interactions. This Minireview will highlight recent advancements and publications around physiologically based pharmacokinetic drug-drug interaction modeling, an important area of drug discovery and development research in light of the increasing prevalence of polypharmacology in clinical settings.

Isoform-level expression of the constitutive androstane receptor (CAR or NR1I3) transcription factor better predicts the mRNA expression of the cytochrome P450s in human liver samples

Many factors cause interperson variability in the activity and expression of the cytochrome P450 (CYP) drug-metabolizing enzymes in the liver, leading to variable drug exposure and treatment outcomes. Several liver-enriched transcription factors are associated with CYP expression, with estrogen receptor α (ESR1) and constitutive androstane receptor (CAR or NR1I3) being the 2 top factors. ESR1 and NR1I3 undergo extensive alternative splicing that results in numerous splice isoforms, but how these splice isoforms associate with CYP expression is unknown. Here, we quantified 18 NR1I3 splice isoforms and the 3 most abundant ESR1 isoforms in 260 liver samples derived from African Americans (n = 125) and European Americans (n = 135). Our results showed variable splice isoform populations in the liver for both NR1I3 and ESR1. Multiple linear regression analyses revealed that compared with gene-level NR1I3, isoform-level NR1I3 expression better predicted the mRNA expression of most CYPs and 3 UDP-glucuronosyltransferases (UGTs), whereas ESR1 isoforms improved predictive models for the UGTs and CYP2D6 but not for most CYPs. Also, different NR1I3 isoforms were associated with different CYPs, and the associations varied depending on sample ancestry. Surprisingly, noncanonical NR1I3 isoforms having retained introns (introns 2 or 6) were abundantly expressed and associated with the expression of most CYPs and UGTs, whereas the reference isoform (NR1I3-205) was only associated with CYP2D6. Moreover, NR1I3 isoform diversity increased during the differentiation of induced pluripotent stem cells to hepatocytes, paralleling increasing CYP expression. These results suggest that isoform-level transcription factor expression may help to explain variation in CYP or UGT expression between individuals. SIGNIFICANCE STATEMENT: We quantified 18 NR1I3 splice isoforms and the 3 most abundant ESR1 splice isoforms in 260 liver samples derived from African American and European American donors and found variable NR1I3 and ESR1 splice isoform expression in the liver. Multiple linear regression analysis showed that, compared with gene-level expression, isoform-level expression of NR1I3 and ESR1 better predicted the mRNA expression of some cytochrome P450s and UDP-glucuronosyltransferases, highlighting the importance of isoform-level analyses to enhance our understanding of gene transcriptional regulatory networks controlling the expression of drug-metabolizing enzymes.

Evidence for the formation of 6PPD-quinone from antioxidant 6PPD by cytochrome P450

N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD) as a rubber antioxidant has attracted global concern, since its ozone-oxidation product 6PPD-quinone (6PPDQ) was found to be the primary toxicant responsible for urban runoff mortality syndrome in coho salmon. However, the biotransformation fate and associated toxicological mechanism of 6PPD have not received much study yet. In this work, the in vitro assays showed 6PPD can be transformed into 6PPDQ by cytochromes P450 (CYP450) in human liver microsomes (HLMs) with 0.98 % production rate, and the adducts of 6PPDQ with calf thymus DNA and the N-N coupling product between 6PPD and 6PPDQ were further identified after 6PPD incubation in HLMs. Further evidence for the 6PPDQ formation can be obtained from the in vivo assays that the 6PPDQ-DNA adducts and 6PPD-N-N-6PPDQ dimer were detected in mice by oral gavage with 6PPD, and the latter dimer species was detected as well in 6PPD exposure to zebrafish larvae. Especially, the bioaccumulation property and high reactivity of 6PPDQ result in the continuous formation of the significant DNA adducts and 6PPD-N-N-6PPDQ dimer even in case of low production rate of biotransformation of 6PPD to 6PPDQ, which may provide potentially effective biomarkers for such process. DFT computations revealed the formation mechanism of 6PPDQ is the (N)H-abstraction of 6PPD by CYP450, followed by amino radical rebound at the nearby ortho-carbon, yielding a quinol intermediate due to spin delocalization, that might readily undergo further oxidation by CYP450 into 6PPDQ.

When less is more: The association between the expression of polymorphic CYPs and AFB1-induced HCC

BACKGROUND

An individual's genetic fingerprint is emerging as a pivotal predictor of numerous disease- and treatment-related factors. Single nucleotide polymorphisms (SNPs) in drug-metabolizing enzymes play key roles in an individual's exposure to a malignancy-associated risk, such as Aflatoxin B1 (AFB1)-induced hepatocellular carcinoma (HCC).

AIM

This study aimed at reviewing literature on the polymorphisms that exist in CYP enzymes and their possible link with susceptibility to AFB1-induced HCC.

MATERIALS & METHODS

A set of keywords associated with the study subject of interest was used to search the Google Scholar and the PubMed database. The last ten years' worth of research projects were included in the results filter. The research involved HCC patients and any connection between polymorphic forms of CYP enzymes and their susceptibility to AFB1-induced HCC, including older but significant data.

RESULTS

Variations in CYP1A2 and CYP3A4 were reported to impact the rate and magnitude of AFB1 bio-activation, thus influencing an individual's vulnerability to develop HCC. In HCC patients, the activity of CYP isoforms varies, where increased activity has been reported with CYP2C9, CYP2D6, and CYP2E1, while CYP1A2, CYP2C8, and CYP2C19 exhibit decreased activity. CYP2D6*10 frequency has been discovered to differ considerably in HCC patients. Rs2740574 (an upstream polymorphism in CYP3A4 as detected in CYP3A4*1B) and rs776746 (which affects CYP3A5 RNA splicing), both of which influence CYP3A expression, thus impacting the variability of AFB1-epoxide adducts in HCC patients.

DISCUSSION

CYP1A2 is the primary enzyme accountable for the formation of harmful AFBO globally. CYP3A4, CYP3A5, CYP3A7, CYP2B7, and CYP3A3 are also implicated in the bio-activation of AFB1 to mutagenic metabolites. It is thought that CYP3A4 is the protein that interacts with AFB1 metabolism the most.

CONCLUSION

Polymorphic variants of CYP enzymes have a functional impact on the susceptibility to AFB1-induced HCC. Outlining such variation and their implications may provide deeper insights into approaching HCC in a more personalized manner for guiding future risk-assessment, diagnosis, and treatment.

Bacterial expression, purification, and characterization of human cytochrome P450 3A4 without N-terminal modifications

In this communication we reported a bacterial system that over-expressed full-length wild-type (WT) human CYP3A4 in Escherichia coli (E. coli) at a level of 495 nmol/L culture. This level of expression was achieved by cloning the cDNA sequence of CYP3A4 WT to a pLW01-P450 vector and co-expressing it with chaperones GroEL/ES in bacterial C41(DE3) cells. Aided with a C-terminal His5-tag, the expressed CYP3A4 WT was purified to homogeneity with a specific content of 14.3 ± 2.0 nmole P450/mg protein using a single Ni-Penta agarose column. Like the N-terminal modified form (CYP3A4-NF14), CYP3A4 WT binds substrate testosterone with a typical sigmoidal feature at slightly higher affinity. Functional characterization revealed that CYP3A4 WT exhibited lower testosterone 6β-hydroxylase activities than CYP3A4-NF14 in reconstituted phospholipid systems. In addition, it was found that the 6β-hydroxylase activity of CYP3A4 WT was less dependent on excess cytochrome P450 oxidoreductase (POR), compared with CYP3A4-NF14. These results suggest that the N-terminal membrane anchor of CYP3A4 WT enhances its interactions with POR and marginally increases testosterone binding.

First-in-Class Small Molecule Degrader of Pregnane X Receptor Enhances Chemotherapy Efficacy

Pregnane X receptor (PXR) is a ligand-activated transcription factor that binds diverse compounds and upregulates drug metabolism machinery in response. PXR activation is detrimental to drug efficacy and safety because it reduces active drug concentrations and increases reactive metabolites, leading to toxicity and/or drug-drug interactions. Thus, effort must be expended in drug development pipelines to assess PXR activation by lead candidates and chemically modify agonists to reduce PXR liabilities while maintaining on-target potencies. Coadministration of drugs with PXR antagonists could prevent PXR-mediated metabolism events, but such compounds are rare and may themselves be converted to agonists by metabolic enzymes or PXR mutations. Here, we report the design, synthesis, optimization, and biological validation of proteolysis targeting chimeras that induce PXR degradation through E3 ubiquitin ligase recruitment. PXR degradation blocks agonist-induced gene expression and enhances anticancer effects of the chemotherapy paclitaxel, a known PXR agonist and substrate of downstream metabolic enzymes.

Appropriate use of triazolam in elderly patients considering a quantitative benefit-risk assessment based on the pharmacokinetic-pharmacodynamic modeling and simulation approach supported by real-world data

BACKGROUND

Triazolam is a typical drug commonly used in the elderly; however, there have been concerns about its adverse events resulting from age-related changes in physiological function and drug interactions with concomitant drugs. Thus, updated information contributing to the appropriate use based on the latest pharmacokinetic and post-marketing surveillance methods is needed. In this study, we evaluated the appropriate use of triazolam in the elderly by integrating real-world data with a modeling and simulation approach.

METHODS

The occurrence risk of adverse events in the elderly was evaluated using the spontaneous adverse event reporting regulatory databases from Japan and the United States. Information on drug concentrations and reactions was extracted from previous publications to estimate the threshold for plasma triazolam concentrations that cause adverse events. The pharmacokinetic/pharmacodynamic (PK/PD) model was then constructed, and the dose and administration were evaluated in various situations anticipated in medical practice.

RESULTS

Among all prescriptions, 25.4% were prescribed to individuals aged 80 years or above, and 51.8% were for those aged 70 years or above. A majority of cases involved CYP3A-metabolized drug combinations, accounting for 85.6%. Elderly individuals were at a higher risk of developing delirium and fall-fracture. Based on the constructed PK/PD model, the risk of adverse events increased when the plasma concentration of triazolam exceeded the calculated threshold of 0.44 ng/mL at approximately 6 h after administration. Administering 0.125 mg of triazolam, is half the approved dose for the elderly in Japan was deemed appropriate. Moreover, there was a substantial risk of adverse events even at a dosage of 0.0625 mg in combination with a moderate or strong inhibitor of cytochrome P450 3 A.

CONCLUSION

Analyzing large-scale databases and existing research publications on PK/PD can practically contribute to optimizing triazolam drug therapy for the elderly in the daily clinical setting.

Extending the shelf life of HLM chips through freeze-drying of human liver microsomes immobilized onto thiol-ene micropillar arrays

Microfluidic flow reactors functionalized with immobilized human liver microsomes (HLM chips) represent a powerful tool for drug discovery and development by enabling mechanism-based enzyme inhibition studies under flow-through conditions. Additionally, HLM chips may be exploited in streamlined production of human drug metabolites for subsequent microfluidic in vitro organ models or as metabolite standards for drug safety assessment. However, the limited shelf life of the biofunctionalized microreactors generally poses a major barrier to their commercial adaptation in terms of both storage and shipping. The shelf life of the HLM chips in the wetted state is ca. 2-3 weeks only and requires cold storage at 4 °C. In this study, we developed a freeze-drying method for lyophilization of HLMs that are readily immobilized inside microfluidic pillar arrays made from off-stoichiometric thiol-ene polymer. The success of lyophilization was evaluated by monitoring the cytochrome P450 and UDP-glucuronosyltransferase enzyme activities of rehydrated HLMs for several months post-freeze-drying. By adapting the freeze-drying protocol, the HLM chips could be stored at room temperature (protected from light and moisture) for at least 9 months (n = 2 independent batches) and up to 16 months at best, with recovered enzyme activities within 60-120% of the non-freeze-dried control chips. This is a major improvement over the cold-storage requirement and the limited shelf life of the non-freeze-dried HLM chips, which can significantly ease the design of experiments, decrease energy consumption during storage, and reduce the shipping costs with a view to commercial adaptation.

Beyond cytotoxic potency: disposition features required to design ADC payload

1. Antibody-drug conjugates (ADCs) have demonstrated impressive clinical usefulness in treating several types of cancer, with the notion of widening of the therapeutic index of the cytotoxic payload through the minimisation of the systemic toxicity. Therefore, choosing the most appropriate payload molecule is a particularly important part of the early design phase of ADC development, especially given the highly competitive environment ADCs find themselves in today.2. The focus of the current review is to describe critical attributes/considerations needed in the discovery and ultimately development of cytotoxic payloads in support of ADC design. In addition to potency, several key dispositional characteristics including solubility, permeability and bystander effect, pharmacokinetics, metabolism, and drug-drug interactions, are described as being an integral part of the integrated activities required in the design of clinically safe and useful ADC therapeutic agents.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models

Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug's physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field.

Construction and application of medication reminder system: intelligent generation of universal medication schedule

BACKGROUND

Patients with chronic conditions need multiple medications daily to manage their condition. However, most patients have poor compliance, which affects the effectiveness of treatment. To address these challenges, we establish a medication reminder system for the intelligent generation of universal medication schedule (UMS) to remind patients with chronic diseases to take medication accurately and to improve safety of home medication.

METHODS

To design medication time constraint with one drug (MTCOD) for each drug and medication time constraint with multi-drug (MTCMD) for each two drugs in order to better regulate the interval and time of patients' medication. Establishment of a medication reminder system consisting of a cloud database of drug information, an operator terminal for medical staff and a patient terminal.

RESULTS

The cloud database has a total of 153,916 pharmaceutical products, 496,708 drug interaction data, and 153,390 pharmaceutical product-ingredient pairs. The MTCOD data was 153,916, and the MTCMD data was 8,552,712. An intelligent UMS medication reminder system was constructed. The system can read the prescription information of patients and provide personalized medication guidance with medication timeline for chronic patients. At the same time, patients can query medication information and get remote pharmacy guidance in real time.

CONCLUSIONS

Overall, the medication reminder system provides intelligent medication reminders, automatic drug interaction identification, and monitoring system, which is helpful to monitor the entire process of treatment in patients with chronic diseases.

Many human pharmaceuticals are weak inhibitors of the cytochrome P450 system in rainbow trout (Oncorhynchus mykiss) liver S9 fractions

Introduction

Pharmaceutical residues are widely detected in aquatic environment and can be taken up by nontarget species such as fish. The cytochromes P450 (CYP) represent an important detoxification mechanism in fish, like in humans. In the present study, we assessed the correlation of the substrate selectivities of rainbow trout CYP1A and CYP3A homologues with those of human, through determination of the half-maximal inhibitory concentrations (IC50) of a total sixteen human pharmaceuticals toward CYP1A-like ethoxyresorufin O-deethylase (EROD) and CYP3A-like 7-benzyloxy-4-trifluoromethylcoumarin O-debenzylase (BFCOD) in rainbow trout (Oncorhynchus mykiss) liver S9 fractions (RT-S9).

Methods

The inhibitory impacts (IC50) of atomoxetine, atorvastatin, azelastine, bimatoprost, clomethiazole, clozapine, desloratadine, disulfiram, esomeprazole, felbinac, flecainide, orphenadrine, prazosin, quetiapine, sulpiride, and zolmitriptan toward the EROD and BFCOD activities in RT-S9 were determined using the IC50 shift assay, capable of identifying time-dependent inhibitors (TDI). Additionally, the nonspecific binding of the test pharmaceuticals to RT-S9 was assessed using equilibrium dialysis.

Results

Most test pharmaceuticals were moderate to weak inhibitors of both EROD and BFCOD activity in RT-S9, even if most are noninhibitors of human CYP1A or CYP3A. Only bimatoprost, clomethiazole, felbinac, sulpiride, and zolmitriptan did not inhibit either activity in RT-S9. EROD inhibition was generally stronger than that of BFCOD and some substances (atomoxetine, flecainide, and prazosin) inhibited selectively only EROD activity. The strongest EROD inhibition was detected with azelastine and esomeprazole (unbound IC50 of 3.8 ± 0.5 µM and 3.0 ± 0.8 µM, respectively). None of the test substances were TDIs of BFCOD, but esomeprazole was a TDI of EROD. Apart from clomethiazole and disulfiram, the nonspecific binding of the test pharmaceuticals to the RT-S9 was extensive (unbound fractions <0.5) and correlated well (R 2 = 0.7135) with their water-octanol distribution coefficients.

Discussion

The results indicate that the P450 interactions in RT-S9 cannot be explicitly predicted based on human data, but the in vitro data reported herein can shed light on the substrate selectivity of rainbow trout CYP1A1 and CYP3A27 in comparison to their human homologues. The IC50 concentrations are however many orders of magnitude higher than average environmental concentrations of pharmaceuticals. The time-dependent EROD inhibition by esomeprazole could warrant further research to evaluate its possible interlinkages with hepatotoxic impacts on fish.

HLM chip - A microfluidic approach to study the mechanistic basis of cytochrome P450 inhibition using immobilized human liver microsomes

Cytochrome P450 (CYP) system is a critical elimination route to most pharmaceuticals in human, but also prone to drug-drug interactions arising from the fact that concomitantly administered pharmaceuticals inhibit one another's CYP metabolism. The most severe form of CYP interactions is irreversible inhibition, which results in permanent inactivation of the critical CYP pathway and is only restored by de novo synthesis of new functional enzymes. In this study, we conceptualize a microfluidic approach to mechanistic CYP inhibition studies using human liver microsomes (HLMs) immobilized onto the walls of a polymer micropillar array. We evaluated the feasibility of these HLM chips for CYP inhibition studies by establishing the stability and the enzyme kinetics for a CYP2C9 model reaction under microfluidic flow and determining the half-maximal inhibitory concentrations (IC50) of three human CYP2C9 inhibitors (sulfaphenazole, tienilic acid, miconazole), including evaluation of their inhibition mechanisms and nonspecific microsomal binding on chip. Overall, the enzyme kinetics of CYP2C9 metabolism on the HLM chip (KM = 127 ± 55 µM) was shown to be similar to that of static HLM incubations (KM = 114 ± 14 µM) and the IC50 values toward CYP2C9 derived from the microfluidic assays (sulfaphenazole 0.38 ± 0.09 µM, tienilic acid 3.4 ± 0.6 µM, miconazole 0.54 ± 0.09 µM) correlated well with those determined using current standard IC50 shift assays. Most importantly, the HLM chip could distinguish between reversible (sulfaphenazole) and irreversible (tienilic acid) enzyme inhibitors in a single, automated experiment, indicating the great potential of the HLM chip to simplify current workflows used in mechanistic CYP inhibition studies. Furthermore, the results suggest that the HLM chip can also identify irreversible enzyme inhibitors, which are not necessarily resulting in a time-dependent inhibition (like suicide inhibitors), but whose inhibition mechanism is based on other kind of covalent or irreversible interaction with the CYP system. With our HLM chip approach, we could identify miconazole as such a compound that nonselectively inhibits the human CYP system with a prolonged, possibly irreversible impact in vitro, even if it is not a time-dependent inhibitor according to the IC50 shift assay.

Development and validation of CYP26A1 inhibition assay for high-throughput screening

All-trans retinoic acid (atRA) is an endogenous ligand of the retinoic acid receptors, which heterodimerize with retinoid X receptors. AtRA is generated in tissues from vitamin A (retinol) metabolism to form a paracrine signal and is locally degraded by cytochrome P450 family 26 (CYP26) enzymes. The CYP26 family consists of three subtypes: A1, B1, and C1, which are differentially expressed during development. This study aims to develop and validate a high throughput screening assay to identify CYP26A1 inhibitors in a cell-free system using a luminescent P450-Glo assay technology. The assay performed well with a signal to background ratio of 25.7, a coefficient of variation of 8.9%, and a Z-factor of 0.7. To validate the assay, we tested a subset of 39 compounds that included known CYP26 inhibitors and retinoids, as well as positive and negative control compounds selected from the literature and/or the ToxCast/Tox21 portfolio. Known CYP26A1 inhibitors were confirmed, and predicted CYP26A1 inhibitors, such as chlorothalonil, prochloraz, and SSR126768, were identified, demonstrating the reliability and robustness of the assay. Given the general importance of atRA as a morphogenetic signal and the localized expression of Cyp26a1 in embryonic tissues, a validated CYP26A1 assay has important implications for evaluating the potential developmental toxicity of chemicals.

Applying Physiologically Based Pharmacokinetic Modeling to Interpret Carbamazepine's Nonlinear Pharmacokinetics and Its Induction Potential on Cytochrome P450 3A4 and Cytochrome P450 2C9 Enzymes

Carbamazepine (CBZ) is commonly prescribed for epilepsy and frequently used in polypharmacy. However, concerns arise regarding its ability to induce the metabolism of other drugs, including itself, potentially leading to the undertreatment of co-administered drugs. Additionally, CBZ exhibits nonlinear pharmacokinetics (PK), but the root causes have not been fully studied. This study aims to investigate the mechanisms behind CBZ's nonlinear PK and its induction potential on CYP3A4 and CYP2C9 enzymes. To achieve this, we developed and validated a physiologically based pharmacokinetic (PBPK) parent-metabolite model of CBZ and its active metabolite Carbamazepine-10,11-epoxide in GastroPlus®. The model was utilized for Drug-Drug Interaction (DDI) prediction with CYP3A4 and CYP2C9 victim drugs and to further explore the underlying mechanisms behind CBZ's nonlinear PK. The model accurately recapitulated CBZ plasma PK. Good DDI performance was demonstrated by the prediction of CBZ DDIs with quinidine, dolutegravir, phenytoin, and tolbutamide; however, with midazolam, the predicted/observed DDI AUClast ratio was 0.49 (slightly outside of the two-fold range). CBZ's nonlinear PK can be attributed to its nonlinear metabolism caused by autoinduction, as well as nonlinear absorption due to poor solubility. In further applications, the model can help understand DDI potential when CBZ serves as a CYP3A4 and CYP2C9 inducer.

Reconstructed Human Skin with Hypodermis Shows Essential Role of Adipose Tissue in Skin Metabolism

BACKGROUND

Dysregulation of skin metabolism is associated with a plethora of diseases such as psoriasis and dermatitis. Until now, reconstructed human skin (RhS) models lack the metabolic potential of native human skin, thereby limiting their relevance to study human healthy and diseased skin. We aimed to determine whether incorporation of an adipocyte-containing hypodermis into RhS improves its metabolic potential and to identify major metabolic pathways up-regulated in adipose-RhS.

METHODS

Primary human keratinocytes, fibroblasts and differentiated adipose-derived stromal cells were co-cultured in a collagen/fibrin scaffold to create an adipose-RhS. The model was extensively characterized structurally in two- and three-dimensions, by cytokine secretion and RNA-sequencing for metabolic enzyme expression.

RESULTS

Adipose-RhS showed increased secretion of adipokines. Both RhS and adipose-RhS expressed 29 of 35 metabolic genes expressed in ex vivo native human skin. Addition of the adipose layer resulted in up-regulation of 286 genes in the dermal-adipose fraction of which 7 were involved in phase I (CYP19A1, CYP4F22, CYP3A5, ALDH3B2, EPHX3) and phase II (SULT2B1, GPX3) metabolism. Vitamin A, D and carotenoid metabolic pathways were enriched. Additionally, pro-inflammatory (IL-1β, IL-18, IL-23, IL-33, IFN-α2, TNF-α) and anti-inflammatory cytokine (IL-10, IL-12p70) secretion was reduced in adipose-RhS.

CONCLUSIONS

Adipose-RhS mimics healthy native human skin more closely than traditional RhS since it has a less inflamed phenotype and a higher metabolic activity, indicating the contribution of adipocytes to tissue homeostasis. Therefore it is better suited to study onset of skin diseases and the effect of xenobiotics.

Complementarity of two proteomic data analysis tools in the identification of drug-metabolising enzymes and transporters in human liver

Several software packages are available for the analysis of proteomic LC-MS/MS data, including commercial (e.g. Mascot/Progenesis LC-MS) and open access software (e.g. MaxQuant). In this study, Progenesis and MaxQuant were used to analyse the same data set from human liver microsomes (n = 23). Comparison focussed on the total number of peptides and proteins identified by the two packages. For the peptides exclusively identified by each software package, distribution of peptide length, hydrophobicity, molecular weight, isoelectric point and score were compared. Using standard cut-off peptide scores, we found an average of only 65% overlap in detected peptides, with surprisingly little consistency in the characteristics of peptides exclusively detected by each package. Generally, MaxQuant detected more peptides than Progenesis, and the additional peptides were longer and had relatively lower scores. Progenesis-specific peptides tended to be more hydrophilic and basic relative to peptides detected only by MaxQuant. At the protein level, we focussed on drug-metabolising enzymes (DMEs) and transporters, by comparing the number of unique peptides detected by the two packages for these specific proteins of interest, and their abundance. The abundance of DMEs and SLC transporters showed good correlation between the two software tools, but ABC showed less consistency. In conclusion, in order to maximise the use of MS datasets, we recommend processing with more than one software package. Together, Progenesis and MaxQuant provided excellent coverage, with a core of common peptides identified in a very robust way.

Acceleration of infectious disease drug discovery and development using a humanized model of drug metabolism

A key step in drug discovery, common to many disease areas, is preclinical demonstration of efficacy in a mouse model of disease. However, this demonstration and its translation to the clinic can be impeded by mouse-specific pathways of drug metabolism. Here, we show that a mouse line extensively humanized for the cytochrome P450 gene superfamily ("8HUM") can circumvent these problems. The pharmacokinetics, metabolite profiles, and magnitude of drug-drug interactions of a test set of approved medicines were in much closer alignment with clinical observations than in wild-type mice. Infection with Mycobacterium tuberculosis, Leishmania donovani, and Trypanosoma cruzi was well tolerated in 8HUM, permitting efficacy assessment. During such assessments, mouse-specific metabolic liabilities were bypassed while the impact of clinically relevant active metabolites and DDI on efficacy were well captured. Removal of species differences in metabolism by replacement of wild-type mice with 8HUM therefore reduces compound attrition while improving clinical translation, accelerating drug discovery.

High-throughput assay to simultaneously evaluate activation of CYP3A and the direct and time-dependent inhibition of CYP3A, CYP2C9, and CYP2D6 using liquid chromatography-tandem mass spectrometry

In the early stages of drug discovery, adequate evaluation of the potential drug-drug interactions (DDIs) of drug candidates is important. Several CYP3A activators are known to lead to underestimation of DDIs. These compounds affect midazolam 1'-hydroxylation but not midazolam 4-hydroxylation.We used both metabolic reactions of midazolam to evaluate the activation and inhibition of CYP3A activators simultaneously. For our CYP inhibition assay using cocktail probe substrates, simultaneous liquid chromatography-tandem mass spectrometry monitoring of 1'-hydroxymidazolam and 4-hydroxymidazolam for CYP3A was established in addition to monitoring of 4-hydroxydiclofenac and 1'-hydroxybufuralol for CYP2C9 and CYP2D6.The results of our cocktail inhibition assay were well correlated with those of a single inhibition assay, as were the estimated inhibition parameters for typical CYP3A inhibitors. In our assay, a proprietary compound that activated midazolam 1'-hydroxylation and tended to inhibit 4-hydroxylation was evaluated along with known CYP3A activators. All compounds were well characterised by comparison of the results of midazolam 1'- and 4-hydroxylation.In conclusion, our CYP cocktail inhibition assay can detect CYP3A activation and assess the direct and time-dependent inhibition potentials for CYP3A, CYP2C9, and CYP2D6. This method is expected to be very efficient in the early stages of drug discovery.

Potential Effects of Essential Oil from Plinia cauliflora (Mart.) Kausel on Leishmania: In Vivo, In Vitro, and In Silico Approaches

In the search for new chemotherapeutic alternatives for cutaneous leishmaniasis (CL), essential oils are promising due to their diverse biological potential. In this study, we aimed to investigate the chemical composition and leishmanicidal and anti-inflammatory potential of the essential oil isolated from the leaves of Plinia cauliflora (PCEO). The chemical composition of PCEO showed β-cis-Caryophyllene (24.4%), epi-γ-Eudesmol (8%), 2-Naphthalenemethanol[decahydro-alpha] (8%), and trans-Calamenene (6.6%) as its major constituents. Our results showed that the PCEO has moderate cytotoxicity (CC50) of 137.4 and 143.7 μg/mL on mice peritoneal exudate cells (mPEC) and Vero cells, respectively. The PCEO was able to significantly decrease mPEC infection by Leishmania amazonensis and Leishmania braziliensis. The value of the inhibitory concentration (IC50) on amastigote forms was about 7.3 µg/mL (L. amazonensis) and 7.2 µg/mL (L. braziliensis). We showed that PCEO induced drastic ultrastructural changes in both species of Leishmania and had a high selectivity index (SI) > 18. The in silico ADMET analysis pointed out that PCEO can be used for the development of oral and/or topical formulation in the treatment of CL. In addition, we also demonstrated the in vivo anti-inflammatory effect, with a 95% reduction in paw edema and a decrease by at least 21.4% in migration immune cells in animals treated with 50 mg/kg of PCEO. Taken together, our results demonstrate that PCEO is a promising topical therapeutic agent against CL.

A Review of CYP-Mediated Drug Interactions: Mechanisms and In Vitro Drug-Drug Interaction Assessment

Drug metabolism is a major determinant of drug concentrations in the body. Drug-drug interactions (DDIs) caused by the co-administration of multiple drugs can lead to alteration in the exposure of the victim drug, raising safety or effectiveness concerns. Assessment of the DDI potential starts with in vitro experiments to determine kinetic parameters and identify risks associated with the use of comedication that can inform future clinical studies. The diverse range of experimental models and techniques has significantly contributed to the examination of potential DDIs. Cytochrome P450 (CYP) enzymes are responsible for the biotransformation of many drugs on the market, making them frequently implicated in drug metabolism and DDIs. Consequently, there has been a growing focus on the assessment of DDI risk for CYPs. This review article provides mechanistic insights underlying CYP inhibition/induction and an overview of the in vitro assessment of CYP-mediated DDIs.

Organ-on-chip technology: Opportunities and challenges

Organ-on-chip (OOC) technology is an innovative approach that reproduces human organ structures and functions on microfluidic platforms, offering detailed insights into intricate physiological processes. This technology provides unique advantages over conventional in vitro and in vivo models and thus has the potential to become the new standard for biomedical research and drug screening. In this mini-review, we compare OOCs with conventional models, highlighting their differences, and present several applications of OOCs in biomedical research. Additionally, we highlight advancements in OOC technology, particularly in developing multiorgan systems, and discuss the challenges and future directions of this field.

Transcriptomic sequencing analysis of key long noncoding RNAs and mRNAs expression profiles in postoperative recurrence of hepatocellular carcinoma

BACKGROUND

Recurrence is the main cause of death in hepatocellular carcinoma (HCC) patients after liver resection.

OBJECTIVE

The long non-coding RNAs (lncRNAs) have been reported participated in progression and prognosis of HCC, however, the vital role of lncRNA in postoperative recurrence of HCC has rarely been systematically identified.

METHODS

RNA-sequencing (RNA-seq) was performed between orthotopic model of HCC and hepatoma postoperative recurrent model to comprehensively analyze the integrated transcriptome expression profiles of lncRNA and mRNA. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) was then conducted to quantify the expression levels of DElncRNAs and their target mRNAs.

RESULTS

In our study, 211 lncRNAs (P-value < 0.05) and 1125 mRNAs (P-adjust < 0.05) were significantly differentially expressed (DE) between two groups. Moreover, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that DElncRNAs and DEmRNAs were mainly enriched in lipid metabolism, including Arachidonic acid metabolism, PPAR signaling pathway, Steroid hormone biosynthesis, Linoleic acid metabolism, Inflammatory mediator regulation of TRP channels, and Fatty acid degradation. Furthermore, we constructed lncRNA-mRNA interaction networks and protein-protein interaction (PPI) network, and verified by qRT-PCR, suggesting that increased DEIncRNAs (XLOC_063499 and XLOC_042016) may prevent HCC recurrence after surgery by upregulating on targeted cytochrome P450 (CYP) family genes in the lipid metabolism pathway, such as cyp3a16, cyp3a44, cyp2c39, cyp2c40 and cyp2c68.

CONCLUSION

Overall, Our findings provided new insights for further investigation of biological function in lncRNA related HCC recurrence.

Self-Reporting Molecular Prodrug for In Situ Quantitative Sensing of Drug Release by Ratiometric Photoacoustic Imaging

Understanding the pharmacokinetics of prodrugs in vivo necessitates quantitative, noninvasive, and real-time monitoring of drug release, despite its difficulty. Ratiometric photoacoustic (PA) imaging, a promising deep tissue imaging technology with a unique capacity for self-calibration, can aid in solving this problem. Here, for the first time, a methylamino-substituted Aza-BODIPY (BDP-N) and the chemotherapeutic drug camptothecin (CPT) are joined via a disulfide chain to produce the molecular theranostic prodrug (BSC) for real-time tumor mapping and quantitative visualization of intratumoral drug release using ratiometric PA imaging. Intact BSC has an extremely low toxicity, with a maximum absorption at ∼720 nm; however, endogenous glutathione (GSH), which is overexpressed in tumors, will cleave the disulfide bond and liberate CPT (with full toxicity) and BDP-N. This is accompanied by a significant redshift in absorption at ∼800 nm, resulting in the PA800/PA720 ratio. In vitro, a linear relationship is successfully established between PA800/PA720 values and CPT release rates, and subsequent experiments demonstrate that this relationship can also be applied to the quantitative detection of intratumoral CPT release in vivo. Notably, the novel ratiometric strategy eliminates nonresponsive interference and amplifies the multiples of the signal response to significantly improve the imaging contrast and detection precision. Therefore, this research offers a viable alternative for the design of molecular theranostic agents for the clinical diagnosis and treatment of tumors.

Forward or Backward: Lessons Learned from Small Molecule Drugs Approved by FDA from 2012 to 2022

At every juncture in history, the design and identification of new drugs pose significant challenges. To gain valuable insights for future drug development, we conducted a detailed analysis of New Molecular Entitiy (NME) approved by the Food and Drug Administration (FDA) from 2012 to 2022 and focused on the analysis of first-in-class (FIC) small-molecules from a perspective of a medicinal chemist. We compared the change of numbers between all the FDA-approved NMEs and FIC, which could be more visual to analyze the changing trend of FIC. To get a more visual change of molecular physical properties, we computed the annual average trends in molecular weight for FIC across various therapeutic fields. Furthermore, we consolidated essential information into three comprehensive databases, which covered the indications, canonical SMILES, structural formula, research and development (R&D) institutions, molecular weight, calculated LogP (CLogP), and route of administration on all the small-molecule pharmaceutical. Through the analysis of the database of 11 years of approvals, we forecast the development trend of NME approval in the future.

Role of circadian clock in the chronoefficacy and chronotoxicity of clopidogrel

BACKGROUND AND PURPOSE

The role of circadian locomotor output cycles kaput (CLOCK) in regulating drug chronoefficacy and chronotoxicity remains elusive. Here, we aimed to uncover the impact of CLOCK and dosing time on clopidogrel efficacy and toxicity.

EXPERIMENTAL APPROACH

The antiplatelet effect, toxicity and pharmacokinetics experiments were conducted with Clock-/- mice and wild-type mice, after gavage administration of clopidogrel at different circadian time points. The expression levels of drug-metabolizing enzymes were determined by quantitative polymerase chain reaction (qPCR) and western blotting. Transcriptional gene regulation was investigated using luciferase reporter and chromatin immunoprecipitation assays.

KEY RESULTS

The antiplatelet effect and toxicity of clopidogrel in wild-type mice showed a dosing time-dependent variation. Clock ablation reduced the antiplatelet effect of clopidogrel, but increased clopidogrel-induced hepatotoxicity, with attenuated rhythms of clopidogrel active metabolite (Clop-AM) and clopidogrel, respectively. We found that Clock regulated the diurnal variation of Clop-AM formation by modulating the rhythmic expression of CYP1A2 and CYP3A1, and altered clopidogrel chronopharmacokinetics by regulation of CES1D expression. Mechanistic studies revealed that CLOCK activated Cyp1a2 and Ces1d transcription by directly binding to the enhancer box (E-box) elements in their promoters, and promoted Cyp3a11 transcription through enhancing the transactivation activity of albumin D-site-binding protein (DBP) and thyrotroph embryonic factor (TEF).

CONCLUSIONS AND IMPLICATIONS

CLOCK regulates the diurnal rhythmicity in clopidogrel efficacy and toxicity through regulation of CYP1A2, CYP3A11 and CES1D expression. These findings may contribute to optimizing dosing schedules for clopidogrel and may deepen understanding of the circadian clock and chronopharmacology.

Drug-drug interaction prediction: databases, web servers and computational models

In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug-drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.

In vitro inhibition of human cytochrome P450 enzymes by licoisoflavone B from Glycyrrhiza uralensis Fisch. ex DC

Glycyrrhiza uralensis Fisch. ex DC, one of the 3 pharmacopeial species of licorice and widely used in dietary supplements, can inhibit certain cytochrome P450 (CYP) enzymes. Thereby, G. uralensis preparations have the potential to cause pharmacokinetic drug interactions when consumed along with prescription medicines. One compound (1.34 mg dry weight) responsible for inhibiting CYP2B6, CYP2C8, and CYP2C9 was isolated using bioactivity-guided fractionation from 250 g dried roots, stolons, and rhizomes. The enzyme kinetics and mechanisms of inhibition were determined using human liver microsomes, recombinant enzymes, and UHPLC-MS/MS-based assays. Identified as licoisoflavone B, this compound displayed reversible inhibition of CYP2C8 with an IC50 value of 7.4 ± 1.1 µM and reversible inhibition of CYP2C9 with an IC50 value of 4.9 ± 0.4 µM. The enzyme kinetics indicated that the mechanism of inhibition was competitive for recombinant CYP2C8, with a Ki value of 7.0 ± 0.7 μM, and mixed-type inhibition for recombinant CYP2C9, with a Ki value of 1.2 ± 0.2 μM. Licoisoflavone B moderately inhibited CYP2B6 through a combination of irreversible and reversible mechanisms with an IC50 value of 16.0 ± 3.9 µM.

Preclinical evaluation of chemically reactive metabolites and mitigation of bioactivation in drug discovery

The formation of reactive metabolites (RMs) is thought to be one of the pathogeneses for some idiosyncratic adverse drug reactions (IADRs) which are considered one of the leading causes of some drug attritions and/or recalls. Minimizing or eliminating the formation of RMs via chemical modification is a useful tactic to reduce the risk of IADRs and time-dependent inhibition (TDI) of cytochrome P450 enzymes (CYPs). The RMs should be carefully handled before making a go-no-go decision. Herein, we highlight the role of RMs in the occurrence of IADRs and CYP TDI, the risk of structural alerts, the approaches of RM assessment at the discovery stage and strategies to minimize or eliminate RM liability. Finally, some considerations for developing a RM-positive drug candidate are suggested.

Western Diet Decreases Hepatic Drug Metabolism in Male LDLr−/−ApoB100/100 Mice

Consumption of a Western diet is an important risk factor for several chronic diseases including nonalcoholic fatty liver disease (NAFLD), but its effect on the xenobiotic metabolizing enzyme activities in the liver has been studied incompletely. In this study, male LDLr−/−ApoB100/100 mice were fed with Western diet (WD) or a standard diet for five months to reveal the effects on drug metabolism such as cytochrome P450 (CYP) oxidation and conjugation activities in the liver. Hepatic steatosis, lobular inflammation, and early fibrosis were observed in WD fed mice, but not in chow diet control mice. When compared to the controls, the WD-fed mice had significantly decreased protein-normalized CYP probe activities of 7-ethoxyresorufinO-deethylation (52%), coumarin 7-hydroxylation (26%), 7-hydroxylation of 3-(3-fluoro-4-hydroxyphenyl)-6-methoxycoumarin (70%), 7-hydroxylation of 3-(4-trifluoromethoxyphenyl)-6-methoxycoumarin (78%), 7-hydroxylation of 3-(3-methoxyphenyl)coumarin (81%), and pentoxyresorufin O-depentylation (66%). Increased activity was seen significantly in sulfonation of 3-(4-methylphenyl)-7-hydroxycoumarin (289%) and cytosol catechol O-methyltranferase (COMT, 148%) in the WD group when compared to the controls. In conclusion, the WD-induced steatosis in male LDLr−/−ApoB100/100 mice was associated with decreased CYP oxidation reactions but had no clear effects on conjugation reactions of glucuronidation, sulfonation, and cytosolic catechol O-methylation. Consequently, the WD may decrease the metabolic elimination of drugs compared to healthier low-fat diets.

Evaluation of a Five-Probe Metabolic Control Cocktail in Long-Term Cocultured Human Hepatocytes

Hepatocyte cocultures like HepatoPac have become more frequently used for the assessment of the intrinsic clearance of slowly metabolised drugs during drug discovery due to a superiority in enzymatic activity over time compared to liver microsomal fractions and suspended primary hepatocytes. However, the relatively high cost and practical limitations prevent several quality control compounds to be included in studies and the activities of many important metabolic enzymes are consequently often not monitored. In this study, we have evaluated the possibility for a cocktail approach of quality control compounds in the human HepatoPac system to ensure adequate activity of the major metabolising enzymes. Five reference compounds were selected based on their known metabolic substrate profile in order to capture major CYP and non-CYP metabolic pathways in the incubation cocktail. The intrinsic clearance of the reference compounds when incubated as singlets or in a cocktail was compared and no considerable difference was observed. We show here that a cocktail approach of quality control compounds allows for easy and efficient evaluation of the metabolic competency of the hepatic coculture system over an extended incubation period.

Simplifying the Extended Clearance Concept Classification System (EC3S) to Guide Clearance Prediction in Drug Discovery

PURPOSE

The Extended Clearance Concept Classification System was established as a development-stage tool to provide a framework for identifying fundamental mechanism(s) governing drug disposition in humans. In the present study, the applicability of the EC3S in drug discovery has been investigated. In its current format, the EC3S relies on low-throughput hepatocyte uptake data, which are not frequently generated in a discovery setting.

METHODS

A relationship between hepatocyte uptake clearance and MDCK permeability was first established along with intrinsic clearance from human liver microsomes. The performance of this approach was examined by categorizing 64 drugs into EC3S classes and comparing the predicted major elimination pathway(s) to that observed in humans. As an extension of the work, the ability of the simplified EC3S to predict human systemic clearance based on intrinsic clearance generated using in-vitro metabolic systems was evaluated.

RESULTS

The assessment enabled the use of MDCK permeability and unscaled unbound intrinsic clearance to generate cut-off criteria to categorize compounds into four EC3S classes: Class 12ab, 2cd, 34ab, and 34cd, with major elimination mechanism(s) assigned to each class. The predictivity analysis suggested that systemic clearance could generally be predicted within threefold for EC3S class 12ab and 34ab compounds. For classes 2cd and 34cd, systemic clearance was poorly predicted using in-vitro systems explored in this study.

CONCLUSION

Collectively, our simplified classification approach is expected to facilitate the identification of mechanism(s) involved in drug elimination, faster resolution of in-vitro to in-vivo disconnects, and better design of mechanistic pharmacokinetic studies in drug discovery.

Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-Generated Protein-Ligand Structures: Towards Per-Target Scoring Functions

In recent years, machine learning has been proposed as a promising strategy to build accurate scoring functions for computational docking finalized to numerically empowered drug discovery. However, the latest studies have suggested that over-optimistic results had been reported due to the correlations present in the experimental databases used for training and testing. Here, we investigate the performance of an artificial neural network in binding affinity predictions, comparing results obtained using both experimental protein-ligand structures as well as larger sets of computer-generated structures created using commercial software. Interestingly, similar performances are obtained on both databases. We find a noticeable performance suppression when moving from random horizontal tests to vertical tests performed on target proteins not included in the training data. The possibility to train the network on relatively easily created computer-generated databases leads us to explore per-target scoring functions, trained and tested ad-hoc on complexes including only one target protein. Encouraging results are obtained, depending on the type of protein being addressed.

Acetylcholinesterase inhibition of Alzheimer's disease: identification of potential phytochemicals and designing more effective derivatives to manage disease condition

Alzheimer's disease (AD) is a brain disease characterized by gradual memory loss and cognitive impairments. Acetylcholinesterase (AChE) inhibitors-such as donepezil, memantine, and tacrine-are FDA-approved medications for AD treatment. Due to the lack of their efficacy and higher side effects, many researchers have been searching for effective and safer alternatives. In this study, experimentally proved phytochemicals against brain diseases were screened based on their binding energies to the target site of AChE, pharmacokinetic properties, and drug-likeness. Although some phytochemicals showed higher binding affinities than the control drug (donepezil), they did not show permeability across the blood-brain barrier (BBB). However, berberine, anthocyanin, and diterpene alkaloid can cross the BBB and showed good binding affinities of -10.3, -10.1, and -10.2 kcal/mol, respectively. MD simulation and PCA of the simulation data of the protein and protein-ligand complexes proved that the complexes are stable in the biological environment. A total of 16 derivatives of berberine and 3 derivatives of anthocyanin also showed higher binding energies compared to the binding affinity (-11.5 kcal/mol) of the donepezil. The derivatives were designed by substituting -F, -CF3, -CN, and -NH2, and provided higher docking scores due to increasing of nonbonding interactions. MM/GBSA calculations show that the binding free energies of the best predicted derivatives of diterpene alkaloid, anthocyanin, and berberine (DA22, AC11, and BB40) are -100.4 ± 8.4, -79.3 ± 8.7, and -78.3 ± 10.7 kcal/mol, respectively, with the protein. Overall, this study was successful in finding new, highly effective, and possibly safer inhibitors of AChE.Communicated by Ramaswamy H. Sarma.

DeepIDC: A Prediction Framework of Injectable Drug Combination Based on Heterogeneous Information and Deep Learning

BACKGROUND AND OBJECTIVE

In clinical practice, injectable drug combination (IDC) usually provides good therapeutic effects for patients. Numerous clinical studies have directly indicated that inappropriate IDC generates adverse drug events (ADEs). The clinical application of injections is increasing, and many injections lack relevant combination information. It is still a significant need for experienced clinical pharmacists to participate in evidence-based drug decision making, monitor medication safety, and manage drug interactions. Meanwhile, a large number of injection pairs and dosage combinations limit exhaustive screening. Here, we present a prediction framework, called DeepIDC, that can expediently screen the feasibility of IDCs using heterogeneous information with deep learning. This is the first specific prediction framework to identify IDCs.

METHODS

Since the interaction between the injected drugs may occur in the direct physical and chemical reactions at the time of mixing or may be the indirect interaction of their drug targets and pathways, we used molecular fingerprints, drug-target associations, and drug-pathway associations to convert injections into a string of digital vectors. Then, based on these injection vectors, we combined a bidirectional long short-term memory and a feed-forward neural network to build a prediction model for accurate and instructive prediction of IDC.

RESULTS

In three realistic evaluation scenarios, DeepIDC has achieved ideal prediction results. Furthermore, compared with the other five machine-learning methods, the proposed predictor is more efficient and robust. Among the top 30 potential IDCs of each IDC class predicted by DeepIDC, we found that 9 cases were experimentally verified in the literature or available on Drug.com.

CONCLUSION

The information we extracted in vivo and in vitro can effectively characterize injectable drugs. DeepIDC developed based on deep learning algorithm provides a valuable unified framework for new IDC discovery, which can make up for the lack of IDC information and predict potential IDC events.

Advances in the study of liver microsomes in the in vitro metabolism and toxicity evaluation of foodborne contaminants

Foodborne contaminants are closely related to anthropologic activities and represent an important food safety hazard. The study of metabolic transformation and toxic side effects of foodborne contaminants in the body is important for their safety assessment. Liver microsomes contain a variety of enzymes related to substance metabolism and biotransformation. An in vitro model simulating liver metabolic transformation is associated with a significant advantage in the study of the metabolic transformation mechanisms of contaminants. This review summarizes the recent progress in the application of liver microsomes in metabolic transformation and toxicity evaluation of various foodborne pollutants based on metabolic kinetics, molecular docking and enzyme inhibition studies. The purpose of this review is to distinguish the existing studies involving liver microsomes and provide strategies for their application in the future. Finally, the prospects and challenges of the liver microsomal model are discussed.

Physiologically Based Pharmacokinetic Modeling To Predict Drug-Biologic Interactions with Cytokine Modulators: Are These Relevant and Is Interleukin-6 Enough?

Drugs that modulate cytokine levels are often used for the treatment of cancer as well as inflammatory or immunologic disorders. Pharmacokinetic drug-biologic interactions (DBIs) may arise from suppression or elevation of cytochrome P450 (P450) enzymes caused by the increase or decrease in cytokine levels after administration of these therapies. There is in vitro and in vivo evidence that demonstrates a clear link between raised interleukin (IL)-6 levels and P450 suppression, in particular CYP3A4. However, despite this, the changes in IL-6 levels in vivo rarely lead to significant drug interactions (area under the curve and Cmax ratios < 2-fold). The clinical significance of such interactions therefore remains questionable and is dependent on the therapeutic index of the small molecule therapy. Physiologically based pharmacokinetic (PBPK) modeling has been used successfully to predict the impact of raised IL-6 on P450 activities. Beyond IL-6, published data show little evidence that IL-8, IL-10, and IL-17 suppress P450 enzymes. In vitro data suggest that IL-1β, IL-2, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ can cause suppression of P450 enzymes. Despite in vivo there being a link between IL-6 levels and P450 suppression, the evidence to support a direct effect of IL-2, IL-8, IL-10, IL-17, IFN-γ, TNF-α, or vascular endothelial growth factor on P450 activity is inconclusive. This commentary will discuss the relevance of such drug-biologic interactions and whether current PBPK models considering only IL-6 are sufficient. SIGNIFICANCE STATEMENT: This commentary summarizes the current in vitro and in vivo literature regarding cytokine-mediated cytochrome P450 suppression and compares the relative suppressive potential of different cytokines in reference to interleukin (IL)-6. It also discusses the relevance of drug-biologic interactions to therapeutic use of small molecule drugs and whether current physiologically based pharmacokinetic models considering only IL-6 are sufficient to predict the extent of drug-biologic interactions.

Unprescribed and unnoticed: Retrospective chart review of adverse events of interactions between antidepressants and over-the-counter drugs

Aim: To systematically evaluate prevalence and clinical characteristics of adverse effects of antidepressants and OTC drugs interactions in a retrospective chart review.

Methodology: Dataset of 1,145 registered adverse events were evaluated. Reports were selected for further analysis if pharmacoepidemiological avaluation indicated the presence of high probability of a causal relationship between antidepressants and OTC interaction and the occurrence of side effect. Following variables were extracted from the records: sex, age, medical comorbidities, antidepressant and other concomitant medications, clinical consequences ant the possible interaction mechanisms.

Results: 368 showed causal relationship with the simultaneous use of antidepressant with another drug. 15 adverse events (4%) were related to the use of OTC medicine, particularly omeprazole, diphenhydramine, Japanese ginkgo biloba, ibuprofen, diclofenac and sildenafil. All of the analysed side effects were categorized as the result of pharmacokinetic interactions. Here we report identified OTC drugs with corresponding antidepressants and clinical manifestations of DDI. Omeprazole: agomelatine (nausea, abnormal dreams), fluoxetine (extrapyramidal symptoms, paresthesias), sertraline (vertigo, yawning), escitalopram (oral vesiculation). Diphenhydramine: sertraline (diaphoresis, insomnia, vertigo), paroxetine (pruritus, headache), duloxetine (oropharyngeal pain). Japanese ginkgo biloba: citalopram (bradycardia), trazodone (vertigo, taste pervesion), mianserine (restless legs syndrome). Diclofenac: escitalopram (oral vesiculation), and fluoxetine (restless legs syndrome). Ibuprofen: agomelatine (anxiety and nausea), sertraline and omeprazole (QTc prolongation). Sildenafil: fluoxetine (genital oedema) and sertraline (myocardial infarction).

Conclusion: The use of OTC drugs by the patients should be monitored. Pharmacokinetic interactions between nonprescribed medicines and antidepressants may increase concentration and severity of side effects of latter ones.

Label-free chemical imaging of cytochrome P450 activity by Raman microscopy

Although investigating drug modulation of cytochrome P450 (CYP) activity under physiological conditions is crucial in drug development to avoid severe adverse drug reactions, the current evaluation approaches that rely on the destructive and end-point analysis can be misleading due to invasive treatments and cellular heterogeneity. Here, we propose a non-destructive and high-content method for visualizing and quantifying intracellular CYP activity under drug administration by Raman microscopy. The redox-state and spin-state sensitive Raman measurement indicated that the induced CYPs in living hepatocytes were in oxidized and low-spin state, which is related to monooxygenase function of CYP. Moreover, glycogen depletion associated with CYP induction was simultaneously observed, indicating a relevant effect on glucose metabolism. By deciphering the overall changes in the biochemical fingerprints of hepatocytes, Raman microscopy offers a non-destructive and quantitative chemical imaging method to evaluate CYP activity at the single-cell level with the potential to facilitate future drug development schemes.

Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies

Cytochrome P450 3A5 (CYP3A5) is one of the crucial CYP family members and has already proven to be an important drug target for cardiovascular diseases. In the current study, the PubChem database was screened through molecular docking and high-affinity molecules were adopted for further assessment. A negative image-based (NIB) model was used for a similarity search by considering the complementary shape and electrostatics of the target and small molecules. Further, the molecules were segregated into active and inactive groups through six machine learning (ML) matrices. The active molecules found in each ML model were used for in silico pharmacokinetics and toxicity assessments. A total of five molecules followed the acceptable pharmacokinetics and toxicity profiles. Several potential binding interactions between the proposed molecules and CYP3A5 were observed. The dynamic behavior of the selected molecules in the CYP3A5 was explored through a molecular dynamics (MD) simulation study. Several parameters obtained from the MD simulation trajectory explained the stability of the protein–ligand complexes in dynamic states. The high binding affinity of each molecule was revealed by the binding free energy calculation through the MM-GBSA methods. Therefore, it can be concluded that the proposed molecules might be potential CYP3A5 molecules for therapeutic application in cardiovascular diseases subjected to in vitro/in vivo validations.