A New Data Repository for Pharmacokinetic Natural Product-Drug Interactions: From Chemical Characterization to Clinical Studies

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.

Natural Products Inhibition of Cytochrome P450 2B6 Activity and Methadone Metabolism

Methadone is cleared predominately by hepatic cytochrome P450 (CYP) 2B6-catalyzed metabolism to inactive metabolites. CYP2B6 also catalyzes the metabolism of several other drugs. Methadone and CYP2B6 are susceptible to pharmacokinetic drug-drug interactions. Use of natural products such as herbals and other botanicals is substantial and growing, and concomitant use of prescription medicines and non-prescription herbals is common and may result in interactions, often precipitated by CYP inhibition. Little is known about herbal product effects on CYP2B6 activity, and CYP2B6-catalyzed methadone metabolism. We screened a family of natural product compounds used in traditional medicines, herbal teas, and synthetic analogs of compounds found in plants, including kavalactones, flavokavains, chalcones and gambogic acid, for inhibition of expressed CYP2B6 activity and specifically inhibition of CYP2B6-mediated methadone metabolism. An initial screen evaluated inhibition of CYP2B6-catalyzed 7-ethoxy-4-(trifluoromethyl) coumarin O-deethylation. Hits were further evaluated for inhibition of racemic methadone metabolism, including mechanism of inhibition and kinetic constants. In order of decreasing potency, the most effective inhibitors of methadone metabolism were dihydromethysticin (competitive, K i 0.074 µM), gambogic acid (noncompetitive, K i 6 µM), and 2,2'-dihydroxychalcone (noncompetitive, K i 16 µM). Molecular modeling of CYP2B6-methadone and inhibitor binding showed substrate and inhibitor binding position and orientation and their interactions with CYP2B6 residues. These results show that CYP2B6 and CYP2B6-catalyzed methadone metabolism are inhibited by certain natural products, at concentrations which may be clinically relevant. SIGNIFICANCE STATEMENT: This investigation identified several natural product constituents which inhibit in vitro human recombinant CYP2B6 and CYP2B6-catalyzed N-demethylation of the opioid methadone. The most potent inhibitors (K i) were dihydromethysticin (0.074 µM), gambogic acid (6 µM) and 2,2'-dihydroxychalcone (16 µM). Molecular modeling of ligand interactions with CYP2B6 found that dihydromethysticin and 2,2'-dihydroxychalcone bound at the active site, while gambogic acid interacted with an allosteric site on the CYP2B6 surface. Natural product constituents may inhibit CYP2B6 and methadone metabolism at clinically relevant concentrations.

Broadening the capture of natural products mentioned in FAERS using fuzzy string-matching and a Siamese neural network

Increased sales of natural products (NPs) in the US and growing safety concerns highlight the need for NP pharmacovigilance. A challenge for NP pharmacovigilance is ambiguity when referring to NPs in spontaneous reporting systems. We used a combination of fuzzy string-matching and a neural network to reduce this ambiguity. Our aim is to increase the capture of reports involving NPs in the US Food and Drug Administration Adverse Event Reporting System (FAERS). For this, we utilized Gestalt pattern-matching (GPM) and Siamese neural network (SM) to identify potential mentions of NPs of interest in 389,386 FAERS reports with unmapped drug names. A team of health professionals refined the candidates identified in the previous step through manual review and annotation. After candidate adjudication, GPM identified 595 unique NP names and SM 504. There was little overlap between candidates identified by each (Non-overlapping: GPM 347, SM 248). We identified a total of 686 novel NP names from FAERS reports. Including these names in the FAERS collection yielded 3,486 additional reports mentioning NPs.

Co-consuming green tea with raloxifene decreases raloxifene systemic exposure in healthy adult participants

Green tea is a popular beverage worldwide. The abundant green tea catechin (-)-epigallocatechin gallate (EGCG) is a potent in vitro inhibitor of intestinal UDP-glucuronosyltransferase (UGT) activity (Ki  ~2 μM). Co-consuming green tea with intestinal UGT drug substrates, including raloxifene, could increase systemic drug exposure. The effects of a well-characterized green tea on the pharmacokinetics of raloxifene, raloxifene 4'-glucuronide, and raloxifene 6-glucuronide were evaluated in 16 healthy adults via a three-arm crossover, fixed-sequence study. Raloxifene (60 mg) was administered orally with water (baseline), with green tea for 1 day (acute), and on the fifth day after daily green tea administration for 4 days (chronic). Unexpectedly, green tea decreased the geometric mean green tea/baseline raloxifene AUC0-96h ratio to ~0.60 after both acute and chronic administration, which is below the predefined no-effect range (0.75-1.33). Lack of change in terminal half-life and glucuronide-to-raloxifene ratios indicated the predominant mechanism was not inhibition of intestinal UGT. One potential mechanism includes inhibition of intestinal transport. Using established transfected cell systems, a green tea extract normalized to EGCG inhibited 10 of 16 transporters tested (IC50 , 0.37-12 μM). Another potential mechanism, interruption by green tea of gut microbe-mediated raloxifene reabsorption, prompted a follow-up exploratory clinical study to evaluate the potential for a green tea-gut microbiota-drug interaction. No clear mechanisms were identified. Overall, results highlight that improvements in current models and methods used to predict UGT-mediated drug interactions are needed. Informing patients about the risk of co-consuming green tea with raloxifene may be considered.

Broadening the Capture of Natural Products Mentioned in FAERS Using Fuzzy String-Matching and a Siamese Neural Network

Increased sales of natural products (NPs) in the US and growing safety concerns highlight the need for NP pharmacovigilance. A challenge for NP pharmacovigilance is ambiguity when referring to NPs in spontaneous reporting systems. We used a combination of fuzzy string-matching and a neural network to reduce this ambiguity. We aim to increase the capture of reports involving NPs in the US Food and Drug Administration Adverse Event Reporting System (FAERS). Gestalt pattern-matching (GPM) and Siamese neural network (SM) were used to identify potential mentions of NPs of interest in 389,386 FAERS reports with unmapped drug names. We refined the identified candidates through manual review and annotation by health professionals. After adjudication, GPM identified 595 unique NP names and SM 504. There was little overlap between candidates identified by the approaches (Non-overlapping: GPM 347, SM 248). In total, 686 novel NP names were identified in the unmapped FAERS reports. Including these names in the FAERS collection yielded 3,486 additional reports mentioning NPs.

Developing a Knowledge Graph for Pharmacokinetic Natural Product-Drug Interactions

BACKGROUND

Pharmacokinetic natural product-drug interactions (NPDIs) occur when botanical or other natural products are co-consumed with pharmaceutical drugs. With the growing use of natural products, the risk for potential NPDIs and consequent adverse events has increased. Understanding mechanisms of NPDIs is key to preventing or minimizing adverse events. Although biomedical knowledge graphs (KGs) have been widely used for drug-drug interaction applications, computational investigation of NPDIs is novel. We constructed NP-KG as a first step toward computational discovery of plausible mechanistic explanations for pharmacokinetic NPDIs that can be used to guide scientific research.

METHODS

We developed a large-scale, heterogeneous KG with biomedical ontologies, linked data, and full texts of the scientific literature. To construct the KG, biomedical ontologies and drug databases were integrated with the Phenotype Knowledge Translator framework. The semantic relation extraction systems, SemRep and Integrated Network and Dynamic Reasoning Assembler, were used to extract semantic predications (subject-relation-object triples) from full texts of the scientific literature related to the exemplar natural products green tea and kratom. A literature-based graph constructed from the predications was integrated into the ontology-grounded KG to create NP-KG. NP-KG was evaluated with case studies of pharmacokinetic green tea- and kratom-drug interactions through KG path searches and meta-path discovery to determine congruent and contradictory information in NP-KG compared to ground truth data. We also conducted an error analysis to identify knowledge gaps and incorrect predications in the KG.

RESULTS

The fully integrated NP-KG consisted of 745,512 nodes and 7,249,576 edges. Evaluation of NP-KG resulted in congruent (38.98% for green tea, 50% for kratom), contradictory (15.25% for green tea, 21.43% for kratom), and both congruent and contradictory (15.25% for green tea, 21.43% for kratom) information compared to ground truth data. Potential pharmacokinetic mechanisms for several purported NPDIs, including the green tea-raloxifene, green tea-nadolol, kratom-midazolam, kratom-quetiapine, and kratom-venlafaxine interactions were congruent with the published literature.

CONCLUSION

NP-KG is the first KG to integrate biomedical ontologies with full texts of the scientific literature focused on natural products. We demonstrate the application of NP-KG to identify known pharmacokinetic interactions between natural products and pharmaceutical drugs mediated by drug metabolizing enzymes and transporters. Future work will incorporate context, contradiction analysis, and embedding-based methods to enrich NP-KG. NP-KG is publicly available at https://doi.org/10.5281/zenodo.6814507. The code for relation extraction, KG construction, and hypothesis generation is available at https://github.com/sanyabt/np-kg.

Clinical Pharmacokinetic Assessment of Kratom (Mitragyna speciosa), a Botanical Product with Opioid-like Effects, in Healthy Adult Participants

Increasing use of the botanical kratom to self-manage opioid withdrawal and pain has led to increased kratom-linked overdose deaths. Despite these serious safety concerns, rigorous fundamental pharmacokinetic knowledge of kratom in humans remains lacking. We assessed the pharmacokinetics of a single low dose (2 g) of a well-characterized kratom product administered orally to six healthy participants. Median concentration-time profiles for the kratom alkaloids examined were best described by a two-compartment model with central elimination. Pronounced pharmacokinetic differences between alkaloids with the 3S configuration (mitragynine, speciogynine, paynantheine) and alkaloids with the 3R configuration (mitraciliatine, speciociliatine, isopaynantheine) were attributed to differences in apparent intercompartmental distribution clearance, volumes of distribution, and clearance. Based on noncompartmental analysis of individual concentration-time profiles, the 3S alkaloids exhibited a shorter median time to maximum concentration (1–2 vs. 2.5–4.5 h), lower area under the plasma concentration-time curve (430–490 vs. 794–5120 nM × h), longer terminal half-life (24–45 vs. ~12–18 h), and higher apparent volume of distribution during the terminal phase (960–12,700 vs. ~46–130 L) compared to the 3R alkaloids. Follow-up mechanistic in vitro studies suggested differential hepatic/intestinal metabolism, plasma protein binding, blood-to-plasma partitioning, and/or distribution coefficients may explain the pharmacokinetic differences between the two alkaloid types. This first comprehensive pharmacokinetic characterization of kratom alkaloids in humans provides the foundation for further research to establish safety and effectiveness of this emerging botanical product.

Overview of Current Herb-Drug Interaction Databases

An HERB-Drug Interaction (HDI) database is a structured data collection method for HDI information extracted from scattered literatures for quick retrieval. Our review summarized the ten currently available HDI databases, including those databases comprising HDI on the market. A detailed comparison on the scope of monographs, including the nature of content extracted from the original literature and user interfaces of these databases, was performed, and the number of references of fifty popular herbs in each HDI database was counted and presented in a heatmap to give users an intuitive understanding of the focuses of different HDI databases. Since it is well known that the development and maintenance of databases need continuous investment of capital and manpower, the sustainability of these databases was also reviewed and compared. Recently, artificial intelligence (AI) technologies, especially Natural Language Processing (NLP), have been applied to screen specific topics from massive articles and automatically identify the names of drugs and herbs in the literature. However, its application on the labor-intensive extraction and evaluation of HDI-related experimental conditions and results from literature remains limited due to the scarcity of these HDI data and the lack of well-established annotated datasets for these specific NLP recognition tasks. In view of the difficulties faced by current HDI databases and potential expansion of AI application in HDI database development, we propose a standardized format for data reporting and use of Concept Unique Identifier (CUI) for medical terms in the literature to accelerate the structured data collection. SIGNIFICANCE STATEMENT: The worldwide popularity of botanical and/or traditional medicine products has raised safety concerns due to potential HDI. However, the publicly available HDI databases are mostly outdated or incomplete. Through our review of the currently available HDI databases, a clear understanding of the key issues could be obtained and possible solutions to overcome the labour-intensive extraction as well as professional evaluation of information in HDI database development are proposed.

Chronic Cannabis Users: A New Special Population to Consider for Drug Development

The use and acceptance of cannabis, either medically or recreationally, has substantially outpaced the collection of data necessary to evaluate its use in any population. However, the mere widespread availability does not imply the absence of risk or confirmation of efficacy and should not be treated as such. There is enough data to suggest that not only does the potential for pharmacokinetic and metabolic interactions exist, but also that baseline characteristics for a given population could be different in chronic cannabis users. Either or both of these may impact the safety and efficacy profile for any new drug in development. As such, we encourage drug developers to consider that the cannabis user may very well be a special population that warrants its own clinical pharmacology evaluation.

Assessing Transporter-Mediated Natural Product-Drug Interactions Via In vitro-In Vivo Extrapolation: Clinical Evaluation With a Probe Cocktail

The botanical natural product goldenseal can precipitate clinical drug interactions by inhibiting cytochrome P450 (CYP) 3A and CYP2D6. Besides P-glycoprotein, effects of goldenseal on other clinically relevant transporters remain unknown. Established transporter-expressing cell systems were used to determine the inhibitory effects of a goldenseal extract, standardized to the major alkaloid berberine, on transporter activity. Using recommended basic models, the extract was predicted to inhibit the efflux transporter BCRP and uptake transporters OATP1B1/3. Using a cocktail approach, effects of the goldenseal product on BCRP, OATP1B1/3, OATs, OCTs, MATEs, and CYP3A were next evaluated in 16 healthy volunteers. As expected, goldenseal increased the area under the plasma concentration-time curve (AUC_0-inf ) of midazolam (CYP3A; positive control), with a geometric mean ratio (GMR) (90% confidence interval (CI)) of 1.43 (1.35-1.53). However, goldenseal had no effects on the pharmacokinetics of rosuvastatin (BCRP and OATP1B1/3) and furosemide (OAT1/3); decreased metformin (OCT1/2, MATE1/2-K) AUC_0-inf (GMR, 0.77 (0.71-0.83)); and had no effect on metformin half-life and renal clearance. Results indicated that goldenseal altered intestinal permeability, transport, and/or other processes involved in metformin absorption, which may have unfavorable effects on glucose control. Inconsistencies between model predictions and pharmacokinetic outcomes prompt further refinement of current basic models to include differential transporter expression in relevant organs and intestinal degradation/metabolism of the precipitant(s). Such refinement should improve in vitro-in vivo prediction accuracy, contributing to a standard approach for studying transporter-mediated natural product-drug interactions.

Natural Products: Experimental Approaches to Elucidate Disposition Mechanisms and Predict Pharmacokinetic Drug Interactions

Natural products have been used by humans since antiquity for both egregious and beneficial purposes. Regarding the latter, these products have long been valued as a rich source of phytochemicals and developed into numerous life-saving pharmaceutical agents. Today, the sales and use of natural products with purported medicinal qualities continue to increase worldwide. However, natural products are not subject to the same premarket testing requirements as pharmaceutical agents, creating critical gaps in scientific knowledge about their optimal use. In addition, due to the common misperception that "natural" means "safe," patients may supplement or replace their prescription medications with natural products, placing themselves at undue risk for subefficacious pharmacotherapy or potentially toxic exposure. Collectively, with few exceptions, researchers, health care providers, and educators lack definitive information about how to inform consumers, patients, and students in the health professions on the safe and optimal use of these products. Recognition of this deficiency by key stakeholders, including the three pillars of biomedical research-industry, academia, and government-has facilitated multiple collaborations that are actively addressing this fundamental knowledge gap. This special issue contains a collection of articles highlighting the challenges faced by researchers in the field and the use of various experimental systems and methods to improve the mechanistic understanding of the disposition and drug interaction potential of natural products. Continued refinement of existing, and development of new, approaches will progress toward the common overarching goal of improving public health. SIGNIFICANCE STATEMENT: Natural products with purported medicinal value constitute an increasing share of the contemporary health care market. Natural products are not subject to the same premarket testing requirements as drug products, creating fundamental scientific knowledge gaps about the safe and effective use of these products. Collaborations among industrial, academic, and governmental researchers in multiple disciplines are anticipated to provide the definitive information needed to fill these gaps and improve public health.