Multi-factorial pharmacokinetic interactions: unraveling complexities in precision drug therapy

Unravelling the complexities of non-alcoholic steatohepatitis: The role of metabolism, transporters, and herb-drug interactions

Nonalcoholic fatty liver disease (NAFLD) is a mainstream halting liver disease with high prevalence in North America, Europe, and other world regions. It is an advanced form of NAFLD caused by the amassing of fat in the liver and can progress to the more severe form known as non-alcoholic steatohepatitis (NASH). Until recently, there was no authorized pharmacotherapy reported for NASH, and to improve the patient's metabolic syndrome, the focus is mainly on lifestyle modification, weight loss, ensuring a healthy diet, and increased physical activity; however, the recent approval of Rezdiffra (Resmetirom) by the US FDA may change this narrative. As per the reported studies, there is an increased articulation of uptake and efflux transporters of the liver, including OATP and MRP, in NASH, leading to changes in the drug's pharmacokinetic properties. This increase leads to alterations in the pharmacokinetic properties of drugs. Furthermore, modifications in Cytochrome P450 (CYP) enzymes can have a significant impact on these properties. Xenobiotics are metabolized primarily in the liver and constitute liver enzymes and transporters. This review aims to delve into the role of metabolism, transport, and potential herb-drug interactions in the context of NASH.

Advancements and challenges in pharmacokinetic and pharmacodynamic research on the traditional Chinese medicine saponins: a comprehensive review

Recent research on traditional Chinese medicine (TCM) saponin pharmacokinetics has revealed transformative breakthroughs and challenges. The multicomponent nature of TCM makes it difficult to select representative indicators for pharmacokinetic studies. The clinical application of saponins is limited by their low bioavailability and short half-life, resulting in fluctuating plasma concentrations. Future directions should focus on novel saponin compounds utilizing colon-specific delivery and osmotic pump systems to enhance oral bioavailability. Optimizing drug combinations, such as ginsenosides with aspirin, shows therapeutic potential. Rigorous clinical validation is essential for practical applications. This review emphasizes a transformative era in saponin research, highlighting the need for clinical validation. TCM saponin pharmacokinetics, guided by traditional principles, are in development, utilizing multidisciplinary approaches for a comprehensive understanding. This research provides a theoretical basis for new clinical drugs and supports rational clinical medication.

Rifampin- and Silymarin-Mediated Pharmacokinetic Interactions of Exogenous and Endogenous Substrates in a Transgenic OATP1B Mouse Model

Organic anion-transporting polypeptides (OATP) 1B1 and OATP1B3, encoded by the SLCO gene family of the solute carrier superfamily, are involved in the disposition of many exogenous and endogenous compounds. Preclinical rodent models help assess risks of pharmacokinetic interactions, but interspecies differences in transporter orthologs and expression limit direct clinical translation. An OATP1B transgenic mouse model comprising a rodent Slco1a/1b gene cluster knockout and human SLCO1B1 and SLCO1B3 gene insertions provides a potential physiologically relevant preclinical tool to predict pharmacokinetic interactions. Pharmacokinetics of exogenous probe substrates, pitavastatin and pravastatin, and endogenous OATP1B biomarkers, coproporphyrin-I and coproporphyrin-III, were determined in the presence and absence of known OATP/Oatp inhibitors, rifampin or silymarin (an extract of milk thistle [Silybum marianum]), in wild-type FVB mice and humanized OATP1B mice. Rifampin increased exposure of pitavastatin (4.6- and 2.8-fold), pravastatin (3.6- and 2.2-fold), and coproporphyrin-III (1.6- and 2.1-fold) in FVB and OATP1B mice, respectively, but increased coproporphyrin-I AUC0-24h only (1.8-fold) in the OATP1B mice. Silymarin did not significantly affect substrate AUC, likely because the silymarin flavonolignan concentrations were at or below their reported IC50 values for the relevant OATPs/Oatps. Silymarin increased the Cmax of pitavastatin 2.7-fold and pravastatin 1.9-fold in the OATP1B mice. The data of the OATP1B mice were similar to those of the pitavastatin and pravastatin clinical data; however, the FVB mice data more closely recapitulated pitavastatin clinical data than the data of the OATP1B mice, suggesting that the OATP1B mice are a reasonable, though costly, preclinical strain for predicting pharmacokinetic interactions when doses are optimized to achieve clinically relevant plasma concentrations.

Update and Application of a Deep Learning Model for the Prediction of Interactions between Drugs Used by Patients with Multiple Sclerosis

Patients with multiple sclerosis (MS) often take multiple drugs at the same time to modify the course of disease, alleviate neurological symptoms and manage co-existing conditions. A major consequence for a patient taking different medications is a higher risk of treatment failure and side effects. This is because a drug may alter the pharmacokinetic and/or pharmacodynamic properties of another drug, which is referred to as drug-drug interaction (DDI). We aimed to predict interactions of drugs that are used by patients with MS based on a deep neural network (DNN) using structural information as input. We further aimed to identify potential drug-food interactions (DFIs), which can affect drug efficacy and patient safety as well. We used DeepDDI, a multi-label classification model of specific DDI types, to predict changes in pharmacological effects and/or the risk of adverse drug events when two or more drugs are taken together. The original model with ~34 million trainable parameters was updated using >1 million DDIs recorded in the DrugBank database. Structure data of food components were obtained from the FooDB database. The medication plans of patients with MS (n = 627) were then searched for pairwise interactions between drug and food compounds. The updated DeepDDI model achieved accuracies of 92.2% and 92.1% on the validation and testing sets, respectively. The patients with MS used 312 different small molecule drugs as prescription or over-the-counter medications. In the medication plans, we identified 3748 DDIs in DrugBank and 13,365 DDIs using DeepDDI. At least one DDI was found for most patients (n = 509 or 81.2% based on the DNN model). The predictions revealed that many patients would be at increased risk of bleeding and bradycardic complications due to a potential DDI if they were to start a disease-modifying therapy with cladribine (n = 242 or 38.6%) and fingolimod (n = 279 or 44.5%), respectively. We also obtained numerous potential interactions for Bruton's tyrosine kinase inhibitors that are in clinical development for MS, such as evobrutinib (n = 434 DDIs). Food sources most often related to DFIs were corn (n = 5456 DFIs) and cow's milk (n = 4243 DFIs). We demonstrate that deep learning techniques can exploit chemical structure similarity to accurately predict DDIs and DFIs in patients with MS. Our study specifies drug pairs that potentially interact, suggests mechanisms causing adverse drug effects, informs about whether interacting drugs can be replaced with alternative drugs to avoid critical DDIs and provides dietary recommendations for MS patients who are taking certain drugs.

Effects of the CYP3A inhibitors, voriconazole, itraconazole, and fluconazole on the pharmacokinetics of osimertinib in rats

Background

Osimertinib, as third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), is the first-line treatment approved to treat advanced T790M mutation-positive tumors. Triazole antifungals are therapeutic drugs for cancer patients to reduce the risk of opportunistic fungal infections. Our objective was to investigate whether three triazole antifungals (voriconazole, itraconazole, and fluconazole) could change the pharmacokinetics of osimertinib in rats.

Methods

The adult male Sprague-Dawley rats were randomly divided into four groups (n = 6): control (0.3% CMC-Na), and voriconazole (20 mg/kg), itraconazole (20 mg/kg), or fluconazole (20 mg/kg) combined with osimertinib (10 mg/kg) group. Tail vein blood samples were collected into heparin tubes at various time points within 0-48 h after osimertinib administration. Osimrtinib's plasma concentration was detected using HPLC-MS/MS system equipped with a Waters XBridge C18 column, with the mobile phase consisting of acetonitrile and 0.2% formic acid water at a flow rate of 0.5 mL/min.

Results

Co-administration with voriconazole or fluconazole increased the Cmax of osimertinib by 58.04% and 53.45%, respectively; the AUC0-t increased by 62.56% and 100.98%, respectively. However, when co-administered with itraconazole, the Cmax and AUC0-t of osimertinib only increased by 13.91% and 34.80%, respectively.

Conclusions

Our results revealed that the pharmacokinetics of osimertinib were significantly changed by voriconazole and fluconazole in rats, whereas it was slightly affected by itraconazole. This work will contribute to a more comprehensive understanding of the pharmacokinetic properties of osimertinib when co-administered with triazole antifungals.

Screening for severe drug-drug interactions in patients with multiple sclerosis: A comparison of three drug interaction databases

Background: Patients with multiple sclerosis (MS) often undergo complex treatment regimens, resulting in an increased risk of polypharmacy and potential drug-drug interactions (pDDIs). Drug interaction databases are useful for identifying pDDIs to support safer medication use. Objective: To compare three different screening tools regarding the detection and classification of pDDIs in a cohort of MS patients. Furthermore, we aimed at ascertaining sociodemographic and clinical factors that are associated with the occurrence of severe pDDIs. Methods: The databases Stockley's, Drugs.com and MediQ were used to identify pDDIs by screening the medication schedules of 627 patients. We determined the overlap of the identified pDDIs and the level of agreement in pDDI severity ratings between the three databases. Logistic regression analyses were conducted to determine patient risk factors of having a severe pDDI. Results: The most different pDDIs were identified using MediQ (n = 1,161), followed by Drugs.com (n = 923) and Stockley's (n = 706). The proportion of pDDIs classified as severe was much higher for Stockley's (37.4%) than for Drugs.com (14.4%) and MediQ (0.9%). Overall, 1,684 different pDDIs were identified by at least one database, of which 318 pDDIs (18.9%) were detected with all three databases. Only 55 pDDIs (3.3%) have been reported with the same severity level across all databases. A total of 336 pDDIs were classified as severe (271 pDDIs by one database, 59 by two databases and 6 by three databases). Stockley's and Drugs.com revealed 47 and 23 severe pDDIs, respectively, that were not included in the other databases. At least one severe pDDI was found for 35.2% of the patients. The most common severe pDDI was the combination of acetylsalicylic acid with enoxaparin, and citalopram was the drug most frequently involved in different severe pDDIs. The strongest predictors of having a severe pDDI were a greater number of drugs taken, an older age, living alone, a higher number of comorbidities and a lower educational level. Conclusions: The information on pDDIs are heterogeneous between the databases examined. More than one resource should be used in clinical practice to evaluate pDDIs. Regular medication reviews and exchange of information between treating physicians can help avoid severe pDDIs.

Drug-Drug Interactions With Over-The-Counter Medicines: Mind the Unprescribed

BACKGROUND

This review provides a summary of the currently available clinical data on drug-drug interactions (DDIs) involving over-the-counter (OTC) medicines. It aims to educate and increase awareness among health care providers and to support decisions in daily practice.

METHODS

An extensive literature search was performed using bibliographic databases available through PubMed.gov. An initial structured search was performed using the keywords "drug-drug-interaction AND (over-the-counter OR OTC)," without further restrictions except for the language. The initial results were screened for all described DDIs involving OTC drugs, and further information was gathered specifically on these drugs using dedicated database searches and references found in the bibliography from the initial hits.

RESULTS

From more than 1200 initial hits (1972-June 2021), 408 relevant publications were screened for DDIs involving OTC drugs, leading to 2 major findings: first, certain types of drug regimens are more prone to DDIs or have more serious DDI-related consequences, such as antiretroviral, anti-infective, and oral anticancer therapies. Second, although most DDIs involve OTC drugs as the perpetrators, some prescription drugs (statins or phosphodiesterase-5 inhibitors) that currently have OTC status can be identified as the victims in DDIs. The following groups were identified to be frequently involved in DDIs: nonsteroidal anti-inflammatory drugs, food supplements, antacids, proton-pump inhibitors, H2 antihistamines, laxatives, antidiarrheal drugs, and herbal drugs.

CONCLUSIONS

The most significant finding was the lack of high-quality evidence for commonly acknowledged interactions. High-quality interaction studies involving different phenotypes in drug metabolism (cytochrome P450) and distribution (transporters) are urgently needed. This should include modern and critical drugs, such as oral anticancer medications and direct oral anticoagulants.

Differential Therapeutic Effects of FXR Activation, sEH Inhibition, and Dual FXR/sEH Modulation in NASH in Diet-Induced Obese Mice

Nonalcoholic fatty liver disease (NAFLD) is an epidemic chronic liver disease and may progress over nonalcoholic steatohepatitis (NASH) to liver cirrhosis and hepatocellular carcinoma. The multiple metabolic, environmental, and genetic factors that are involved in NAFLD/NASH pathogenesis and progression suggest a need for multimechanistic interventions. We have developed and preliminarily characterized a concept of dual farnesoid X receptor (FXR) and soluble epoxide hydrolase (sEH) modulation as a promising polypharmacological strategy to counteract NASH. Here we report the profiling of FXR activation, sEH inhibition, and simultaneous FXR/sEH modulation as an interventional treatment in pre-established NASH in mice with diet-induced obesity (DIO). We found that full FXR activation was required to obtain antisteatosis effects but also worsened ballooning degeneration and fibrosis. In contrast, sEH inhibition and dual FXR/sEH modulation, despite a lack of antisteatosis activity, had anti-inflammatory effects and efficiently counteracted hepatic fibrosis. These results demonstrate great therapeutic potential of sEH inhibition to counteract hepatic fibrosis and validate the designed polypharmacology concept of dual FXR/sEH modulation as a potentially superior avenue for the effective treatment of the multifactorial condition NASH.