Structural basis for the severe adverse interaction of sofosbuvir and amiodarone on L-type Cav channels.

Drug-drug interaction of the antiviral sofosbuvir and the antiarrhythmics amiodarone has been reported to cause fatal heartbeat slowing. Sofosbuvir and its analog, MNI-1, were reported to potentiate the inhibition of cardiomyocyte calcium handling by amiodarone, which functions as a multi-channel antagonist, and implicate its inhibitory effect on L-type Cav channels, but the molecular mechanism has remained unclear. Here we present systematic cryo-EM structural analysis of Cav1.1 and Cav1.3 treated with amiodarone or sofosbuvir alone, or sofosbuvir/MNI-1 combined with amiodarone. Whereas amiodarone alone occupies the dihydropyridine binding site, sofosbuvir is not found in the channel when applied on its own. In the presence of amiodarone, sofosbuvir/MNI-1 is anchored in the central cavity of the pore domain through specific interaction with amiodarone and directly obstructs the ion permeation path. Our study reveals the molecular basis for the physical, pharmacodynamic interaction of two drugs on the scaffold of Cav channels.

Oral Anticoagulants Beyond Warfarin.

Direct oral anticoagulants (DOACs) have largely replaced vitamin K antagonists, mostly warfarin, for the main indications for oral anticoagulation, prevention and treatment of venous thromboembolism, and prevention of embolic stroke in atrial fibrillation. While DOACs offer practical, fixed-dose anticoagulation in many patients, specific restrictions or contraindications may apply. DOACs are not sufficiently effective in high-thrombotic risk conditions such as antiphospholipid syndrome and mechanical heart valves. Patients with cancer-associated thrombosis may benefit from DOACs, but the bleeding risk, particularly in those with gastrointestinal or urogenital tumors, must be carefully weighed. In patients with frailty, excess body weight, and/or moderate-to-severe chronic kidney disease, DOACs must be cautiously administered and may require laboratory monitoring. Reversal agents have been developed and approved for life-threatening bleeding. In addition, the clinical testing of potentially safer anticoagulants such as factor XI(a) inhibitors is important to further optimize anticoagulant therapy in an increasingly elderly and frail population worldwide.

Drug-drug interactions and synergy: from pharmacological models to clinical application.

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts such as concentration-response curves, additive effects, and predictive models are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. While various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected at bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care. Significance Statement Combining drugs with different mechanisms of action for synergistic interactions optimizes treatment efficacy and safety. Accurate interpretation of synergy requires the identification of the expected additive effect. Despite innovative models to predict the additive effect, consensus in drug interaction research is lacking, hindering the bench-to-bedside development of combination therapies. Collaboration among science, industry, and regulation is crucial for advancing combination therapy development, ensuring rigorous application of predictive models in clinical settings.

Roles of Individual Human Cytochrome P450 Enzymes in Drug Metabolism.

Our knowledge of the roles of individual cytochrome P450 (P450, CYP) enzymes in drug metabolism has developed considerably in the past 30 years, and this base has been of considerable use in avoiding serious issues with drug interactions and issues due to variations. Some newer approaches are being considered for "phenotyping" of metabolism reactions with new drug candidates. Endogenous biomarkers are being used for non-invasive estimation of levels of individual P450 enzymes. There is also the matter of some remaining "orphan" P450s, which have yet to be assigned reactions. Practical problems that continue in drug development include predicting drug-drug interactions, predicting the effects of polymorphic and other P450 variations, and evaluating inter-species differences in drug metabolism, particularly in the context of "metabolism in safety testing" (MIST) regulatory issues ("disproportionate (human) metabolites"). Significance Statement Cytochrome P450 enzymes are the major catalysts involved in drug metabolism. The characterization of their individual roles has major implications in drug development and clinical practice.

DDID: a comprehensive resource for visualization and analysis of diet-drug interactions.

Diet-drug interactions (DDIs) are pivotal in drug discovery and pharmacovigilance. DDIs can modify the systemic bioavailability/pharmacokinetics of drugs, posing a threat to public health and patient safety. Therefore, it is crucial to establish a platform to reveal the correlation between diets and drugs. Accordingly, we have established a publicly accessible online platform, known as Diet-Drug Interactions Database (DDID, https://bddg.hznu.edu.cn/ddid/), to systematically detail the correlation and corresponding mechanisms of DDIs. The platform comprises 1338 foods/herbs, encompassing flora and fauna, alongside 1516 widely used drugs and 23 950 interaction records. All interactions are meticulously scrutinized and segmented into five categories, thereby resulting in evaluations (positive, negative, no effect, harmful and possible). Besides, cross-linkages between foods/herbs, drugs and other databases are furnished. In conclusion, DDID is a useful resource for comprehending the correlation between foods, herbs and drugs and holds a promise to enhance drug utilization and research on drug combinations.

Harnessing large language models' zero-shot and few-shot learning capabilities for regulatory research.

Large language models (LLMs) are sophisticated AI-driven models trained on vast sources of natural language data. They are adept at generating responses that closely mimic human conversational patterns. One of the most notable examples is OpenAI's ChatGPT, which has been extensively used across diverse sectors. Despite their flexibility, a significant challenge arises as most users must transmit their data to the servers of companies operating these models. Utilizing ChatGPT or similar models online may inadvertently expose sensitive information to the risk of data breaches. Therefore, implementing LLMs that are open source and smaller in scale within a secure local network becomes a crucial step for organizations where ensuring data privacy and protection has the highest priority, such as regulatory agencies. As a feasibility evaluation, we implemented a series of open-source LLMs within a regulatory agency's local network and assessed their performance on specific tasks involving extracting relevant clinical pharmacology information from regulatory drug labels. Our research shows that some models work well in the context of few- or zero-shot learning, achieving performance comparable, or even better than, neural network models that needed thousands of training samples. One of the models was selected to address a real-world issue of finding intrinsic factors that affect drugs' clinical exposure without any training or fine-tuning. In a dataset of over 700 000 sentences, the model showed a 78.5% accuracy rate. Our work pointed to the possibility of implementing open-source LLMs within a secure local network and using these models to perform various natural language processing tasks when large numbers of training examples are unavailable.

Computational prediction of interactions between Paxlovid and prescription drugs.

Pfizer's Paxlovid has recently been approved for the emergency use authorization (EUA) from the US Food and Drug Administration (FDA) for the treatment of mild-to-moderate COVID-19. Drug interactions can be a serious medical problem for COVID-19 patients with underlying medical conditions, such as hypertension and diabetes, who have likely been taking other drugs. Here, we use deep learning to predict potential drug-drug interactions between Paxlovid components (nirmatrelvir and ritonavir) and 2,248 prescription drugs for treating various diseases.

Bridging the maytansine and vinca sites: Cryptophycins target ß-tubulin's T5-loop.

Cryptophycins are microtubule-targeting agents (MTAs) that belong to the most potent antimitotic compounds known to date; however, their exact molecular mechanism of action remains unclear. Here, we present the 2.2 Å resolution X-ray crystal structure of a potent cryptophycin derivative bound to the αß-tubulin heterodimer. The structure addresses conformational issues present in a previous 3.3 Å resolution cryo-electron microscopy structure of cryptophycin-52 bound to the maytansine site of ß-tubulin. It further provides atomic details on interactions of cryptophycins, which had not been described previously, including ones that are in line with structure-activity relationship studies. Interestingly, we discovered a second cryptophycin-binding site that involves the T5-loop of ß-tubulin, a critical secondary structure element involved in the exchange of the guanosine nucleotide and in the formation of longitudinal tubulin contacts in microtubules. Cryptophycins are the first natural ligands found to bind to this new "ßT5-loop site" that bridges the maytansine and vinca sites. Our results offer unique avenues to rationally design novel MTAs with the capacity to modulate T5-loop dynamics and to simultaneously engage multiple ß-tubulin binding sites.

Wiring Between Close Nodes in Molecular Networks Evolves More Quickly Than Between Distant Nodes.

As species diverge, a wide range of evolutionary processes lead to changes in protein-protein interaction (PPI) networks and metabolic networks. The rate at which molecular networks evolve is an important question in evolutionary biology. Previous empirical work has focused on interactomes from model organisms to calculate rewiring rates, but this is limited by the relatively small number of species and sparse nature of network data across species. We present a proxy for variation in network topology: variation in drug-drug interactions (DDIs), obtained by studying drug combinations (DCs) across taxa. Here, we propose the rate at which DDIs change across species as an estimate of the rate at which the underlying molecular network changes as species diverge. We computed the evolutionary rates of DDIs using previously published data from a high-throughput study in gram-negative bacteria. Using phylogenetic comparative methods, we found that DDIs diverge rapidly over short evolutionary time periods, but that divergence saturates over longer time periods. In parallel, we mapped drugs with known targets in PPI and cofunctional networks. We found that the targets of synergistic DDIs are closer in these networks than other types of DCs and that synergistic interactions have a higher evolutionary rate, meaning that nodes that are closer evolve at a faster rate. Future studies of network evolution may use DC data to gain larger-scale perspectives on the details of network evolution within and between species.

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction.

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, natural language processing based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.

DBGRU-SE: predicting drug-drug interactions based on double BiGRU and squeeze-and-excitation attention mechanism.

The prediction of drug-drug interactions (DDIs) is essential for the development and repositioning of new drugs. Meanwhile, they play a vital role in the fields of biopharmaceuticals, disease diagnosis and pharmacological treatment. This article proposes a new method called DBGRU-SE for predicting DDIs. Firstly, FP3 fingerprints, MACCS fingerprints, Pubchem fingerprints and 1D and 2D molecular descriptors are used to extract the feature information of the drugs. Secondly, Group Lasso is used to remove redundant features. Then, SMOTE-ENN is applied to balance the data to obtain the best feature vectors. Finally, the best feature vectors are fed into the classifier combining BiGRU and squeeze-and-excitation (SE) attention mechanisms to predict DDIs. After applying five-fold cross-validation, The ACC values of DBGRU-SE model on the two datasets are 97.51 and 94.98%, and the AUC are 99.60 and 98.85%, respectively. The results showed that DBGRU-SE had good predictive performance for drug-drug interactions.

Learning self-supervised molecular representations for drug-drug interaction prediction.

Drug-drug interactions (DDI) are a critical concern in healthcare due to their potential to cause adverse effects and compromise patient safety. Supervised machine learning models for DDI prediction need to be optimized to learn abstract, transferable features, and generalize to larger chemical spaces, primarily due to the scarcity of high-quality labeled DDI data. Inspired by recent advances in computer vision, we present SMR-DDI, a self-supervised framework that leverages contrastive learning to embed drugs into a scaffold-based feature space. Molecular scaffolds represent the core structural motifs that drive pharmacological activities, making them valuable for learning informative representations. Specifically, we pre-trained SMR-DDI on a large-scale unlabeled molecular dataset. We generated augmented views for each molecule via SMILES enumeration and optimized the embedding process through contrastive loss minimization between views. This enables the model to capture relevant and robust molecular features while reducing noise. We then transfer the learned representations for the downstream prediction of DDI. Experiments show that the new feature space has comparable expressivity to state-of-the-art molecular representations and achieved competitive DDI prediction results while training on less data. Additional investigations also revealed that pre-training on more extensive and diverse unlabeled molecular datasets improved the model's capability to embed molecules more effectively. Our results highlight contrastive learning as a promising approach for DDI prediction that can identify potentially hazardous drug combinations using only structural information.

Effectiveness of double-dose dolutegravir in people receiving rifampin-based tuberculosis treatment: an observational, cohort study of people with HIV from six countries.

BACKGROUND: Tenofovir-lamivudine-dolutegravir (TLD) is the preferred first-line antiretroviral therapy (ART) regimen. An additional 50 mg dose of dolutegravir (TLD + 50) is required with rifampin-containing tuberculosis (TB) co-treatment. There are limited data on the effectiveness of TLD + 50 in individuals with TB/HIV. METHODS: Prospective, observational cohort study at 12 sites in Haiti, Kenya, Malawi, South Africa, Uganda, Zimbabwe. Participants starting TLD and rifampin-containing TB treatment were eligible. Primary outcome was HIV-1 RNA ≤1000 copies/mL at end of TB treatment. FINDINGS: We enrolled 91 participants with TB/HIV: 75 (82%) ART-naïve participants starting TLD after a median 15 days on TB treatment, 10 (11%) ART-naïve participants starting TLD and TB treatment, 5 (5%) starting TB treatment after a median 3.3 years on TLD, and 1 (1%) starting TB treatment and TLD after changing from efavirenz/lamivudine/tenofovir. Median age was 37 years, 35% female, median CD4 count 120 cells/mm3 (IQR 50-295), 87% had HIV-1 RNA >1000 copies/mL. Two participants died during TB treatment. Among 89 surviving participants, 80 were followed to TB treatment completion, including 7 who had no HIV-1 RNA result due to missed visits. Primary virologic outcome was assessed in 73 participants, of whom 69 (95%, 95% CI 89-100%) had HIV-1 RNA ≤1000 copies/mL. No dolutegravir resistance mutations were detected among four participants with HIV-1 RNA >1000 copies/mL. INTERPRETATION: In routine programmatic settings, concurrent rifampin-containing TB treatment and TLD + 50 was feasible, well-tolerated, and achieved high rates of viral suppression in a cohort of predominantly ART-naïve people with TB/HIV.

Lack of Interactions Between Alteplase/Tenecteplase and the Adenosine A1R/A3R Agonist AST-004.

BACKGROUND: AST-004, a small molecule agonist of the adenosine A1 and A3 receptors, is a potential cerebroprotectant for patients with acute stroke and is currently in clinical trials. Drug-drug interactions are critically important to assess in the context of acute stroke care. Lytic therapy with tPA (tissue-type plasminogen activator)-induced plasmin formation (alteplase) is the only available pharmacotherapy for acute stroke. Consequently, it is imperative to evaluate potential interactions between AST-004 and tPAs such as alteplase and tenecteplase. METHODS: The interactions between AST-004 and tPAs were evaluated in 3 ways in preparation for AST-004 phase II trials. First, the metabolic stability of AST-004 was determined in the presence of alteplase and plasmin. Second, the potential for AST-004 to influence the thrombolytic efficacy of alteplase and tenecteplase was evaluated with an in vitro assay system utilizing a fluorogenic substrate of plasmin. Finally, the potential for AST-004 to influence the thrombolytic efficacy of alteplase was also determined with an in vitro thrombolysis assay of human blood thrombi. RESULTS: Neither alteplase nor plasmin affected the stability of AST-004 in vitro. In 2 different in vitro systems, AST-004 had no effect on the ability of alteplase or tenecteplase to generate plasmin, and AST-004 had no effect on the thrombolytic efficacy of alteplase to lyse blood clots in human blood. CONCLUSIONS: These studies indicate that there will be no interactions between AST-004 and tPAs such as alteplase or tenecteplase in patients with stroke undergoing thrombolytic therapy.

Pharmacogenomics of Antiretroviral Drug Metabolism and Transport.

Antiretroviral therapy has markedly reduced morbidity and mortality for persons living with human immunodeficiency virus (HIV). Individual tailoring of antiretroviral regimens has the potential to further improve the long-term management of HIV through the mitigation of treatment failure and drug-induced toxicities. While the mechanisms underlying anti-HIV drug adverse outcomes are multifactorial, the application of drug-specific pharmacogenomic knowledge is required in order to move toward the personalization of HIV therapy. Thus, detailed understanding of the metabolism and transport of antiretrovirals and the influence of genetics on these pathways is important. To this end, this review provides an up-to-date overview of the metabolism of anti-HIV therapeutics and the impact of genetic variation in drug metabolism and transport on the treatment of HIV. Future perspectives on and current challenges in pursuing personalized HIV treatment are also discussed.

Real-world study: Impact of multidisciplinary management of apalutamide-associated adverse events in prostate cancer.

OBJECTIVE: To analyse the adverse events (AEs) associated with apalutamide and the impact of a multidisciplinary team (MDT) protocol on its management at a tertiary care hospital in a real-world setting. METHODS: This was an observational, prospective, cohort study based on real-world evidence at the Hospital Clínic de Barcelona. Includes patients diagnosed with metastatic hormone-sensitive prostate cancer (mHSPC) or high-risk nonmetastatic castration-resistant prostate cancer (nmCRPC) and who started treatment with apalutamide between May 2019 and March 2023 in a real-world clinical setting. RESULTS: Of the 121 patients treated with apalutamide, 52.1% experienced an AE, 19.8% experienced temporarily interruption or a reduction in the dose of apalutamide, and 13.2% discontinued treatment due to AEs. Without MDT protocol (49 patients), 24.5% of patients had to temporarily interrupt or reduce the dose of apalutamide due to AEs, with a median time from the start of treatment of 10.1 months, and 24.5% discontinued apalutamide due to AEs, with a median time from the start of treatment of 3.1 months. Meanwhile, whit MDT protocol (72 patients), 16.7% of patients had to temporarily interrupt or reduce the dose of apalutamide due to AEs, with a median time from the start of treatment of 1.6 months, and 5.6% discontinued apalutamide due to AEs, with a median time from the start of treatment of 4 months. The risk reduction associated with treatment discontinuation was statistically significant (p-value = 0.003). CONCLUSIONS: This study highlights the importance of MDT management of AEs associated with apalutamide to reduce treatment discontinuation.

Ivosidenib significantly reduces triazole levels in patients with acute myeloid leukemia and myelodysplastic syndrome.

BACKGROUND: Ivosidenib is primarily metabolized by CYP3A4; however, it induces CYP450 isozymes, including CYP3A4 and CYP2C9, whereas it inhibits drug transporters, including P-glycoprotein. Patients with acute myeloid leukemia are at risk of invasive fungal infections, and therefore posaconazole and voriconazole are commonly used in this population. Voriconazole is a substrate of CYP2C9, CYP2C19, and CYP3A4; therefore, concomitant ivosidenib may result in decreased serum concentrations. Although posaconazole is a substrate of P-glycoprotein, it is metabolized primarily via UDP glucuronidation; thus, the impact of ivosidenib on posaconazole exposure is unknown. METHODS: Patients treated with ivosidenib and concomitant triazole with at least one serum trough level were included. Subtherapeutic levels were defined as posaconazole <700 ng/mL and voriconazole <1.0 µg/mL. The incidences of breakthrough invasive fungal infections and QTc prolongation were identified at least 5 days after initiation of ivosidenib with concomitant triazole. RESULTS: Seventy-eight serum triazole levels from 31 patients receiving ivosidenib-containing therapy and concomitant triazole were evaluated. Of the 78 concomitant levels, 47 (60%) were subtherapeutic (posaconazole: n = 20 of 43 [47%]; voriconazole: n = 27 of 35 [77%]). Compared to levels drawn while patients were off ivosidenib, median triazole serum levels during concomitant ivosidenib were significantly reduced. There was no apparent increase in incidence of grade 3 QTc prolongation with concomitant azole antifungal and ivosidenib 500 mg daily. CONCLUSIONS: This study demonstrated that concomitant ivosidenib significantly reduced posaconazole and voriconazole levels. Voriconazole should be avoided, empiric high-dose posaconazole (>300 mg/day) may be considered, and therapeutic drug monitoring is recommended in all patients receiving concomitant ivosidenib.

Population pharmacokinetics of rifabutin among HIV/TB co-infected children on lopinavir/ritonavir-based antiretroviral therapy.

In adults requiring protease inhibitor (PI)-based antiretroviral therapy (ART), replacing rifampicin with rifabutin is a preferred option, but there is lack of evidence to guide rifabutin dosing in children, especially with PIs. We aimed to characterize the population pharmacokinetics of rifabutin and 25-O-desacetyl rifabutin (des-rifabutin) in children and optimize its dose. We included children from three age cohorts: (i) <1-year-old cohort and (ii) 1- to 3-year-old cohort, who were ART naïve and received 15- to 20-mg/kg/day rifabutin for 2 weeks followed by lopinavir/ritonavir (LPV/r)-based ART with 5.0- or 2.5 mg/kg/day rifabutin, respectively, while the (iii) >3-year-old cohort was ART-experienced and received 2.5-mg/kg/day rifabutin with LPV/r-based ART. Non-linear mixed-effects modeling was used to interpret the data. Monte Carlo simulations were performed to evaluate the study doses and optimize dosing using harmonized weight bands. Twenty-eight children were included, with a median age of 10 (range 0.67-15.0) years, a median weight of 11 (range 4.5-45) kg, and a median weight-for-age z score of -3.33 (range -5.15 to -1.32). A two-compartment disposition model, scaled allometrically by weight, was developed for rifabutin and des-rifabutin. LPV/r increased rifabutin bioavailability by 158% (95% confidence interval: 93.2%-246.0%) and reduced des-rifabutin clearance by 76.6% (74.4%-78.3%). Severely underweight children showed 26% (17.9%-33.7%) lower bioavailability. Compared to adult exposures, simulations resulted in higher median steady-state rifabutin and des-rifabutin exposures in 6-20 kg during tuberculosis-only treatment with 20 mg/kg/day. During LPV/r co-treatment, the 2.5-mg/kg/day dose achieved similar exposures to adults, while the 5-mg/kg/day dose resulted in higher exposures in children >7 kg. All study doses maintained a median Cmax of <900 µg/L. The suggested weight-band dosing matches adult exposures consistently across weights and simplifies dosing.

Opioid metabolism and drug-drug interaction in cancer.

Concomitant use of multiple drugs in most patients with cancer may result in drug-drug interactions (DDIs), potentially causing serious adverse effects. These patients often experience unrelieved cancer-related pain (CRP) during and after cancer treatment, which can lead to a reduced quality of life. Opioids can be used as part of a multimodal pain management strategy when non-opioid analgesics are not providing adequate pain relief, not tolerated, or are contraindicated. However, due to their narrow therapeutic window, opioids are more susceptible to adverse events when a DDI occurs. Clinically relevant DDIs with opioids are usually pharmacokinetic, mainly occurring via metabolism by cytochrome P450 (CYP). This article aims to provide an overview of potential DDIs with opioids often used in the treatment of moderate-to-severe CRP and commonly used anticancer drugs such as chemotherapeutics, tyrosine kinase inhibitors (TKIs), or biologics. A DDI-checker tool was used to contextualize the tool-informed DDI assessment outcomes with clinical implications and practice. The findings were compared to observations from a literature search conducted in Embase and PubMed to identify clinical evidence for these potential DDIs. The limited results mainly included case studies and retrospective reviews. Some potential DDIs on the DDI-checker were aligned with literature findings, while others were contradictory. In conclusion, while DDI-checkers are useful tools in identifying potential DDIs, it is necessary to incorporate literature verification and comprehensive clinical assessment of the patient before implementing tool-informed decisions in clinical practice.

Effects of cannabidiol and Δ9-tetrahydrocannabinol on cytochrome P450 enzymes: a systematic review.

Due to legal, political, and cultural changes, the use of cannabis has rapidly increased in recent years. Research has demonstrated that the cannabinoids cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC) inhibit and induce cytochrome P450 (CYP450) enzymes. The objective of this review is to evaluate the effect of CBD and THC on the activity of CYP450 enzymes and the implications for drug-drug interactions (DDIs) with psychotropic agents that are CYP substrates. A systematic search was conducted using PubMed, Scopus, Scientific Electronic Library Online (SciELO) and PsychINFO. Search terms included 'cannabidiol', 'tetrahydrocannabinol', and 'cytochrome P450'. A total of seven studies evaluating the interaction of THC and CBD with CYP450 enzymes and psychotropic drugs were included. Both preclinical and clinical studies were included. Results from the included studies indicate that both CBD and THC inhibit several CYP450 enzymes including, but not limited to, CYP1A2, CYP3C19, and CYP2B6. While there are a few known CYP450 enzymes that are induced by THC and CBD, the induction of CYP450 enzymes is an understudied area of research and lacks clinical data. The inhibitory effects observed by CBD and THC on CYP450 enzymes vary in magnitude and may decrease the metabolism of psychotropic agents, cause changes in plasma levels of psychotropic medications, and increase adverse effects. Our findings clearly present interactions between THC and CBD and several CYP450 enzymes, providing clinicians evidence of a high risk of DDIs for patients who consume both cannabis and psychotropic medication. However, more clinical research is necessary before results are applied to clinical settings.