Assessment Of Drug-drug Interactions Research Articles

Early Prediction and Impact Assessment of CYP3A4-Related Drug-Drug Interactions for Small-Molecule Anticancer Drugs Using Human-CYP3A4-Transgenic Mouse Models.

Early detection of drug-drug interactions (DDIs) can facilitate timely drug development decisions, prevent unnecessary restrictions on patient enrollment, resulting in clinical study populations that are not representative of the indicated study population, and allow for appropriate dose adjustments to ensure safety in clinical trials. All of these factors contribute to a streamlined drug approval process and enhanced patient safety. Here we describe a new approach for early prediction of the magnitude of change in exposure for cytochrome P450 (P450) CYP3A4-related DDIs of small-molecule anticancer drugs based on the model-based extrapolation of human-CYP3A4-transgenic mice pharmacokinetics to humans. Victim drugs brigatinib and lorlatinib were evaluated with the new approach in combination with the perpetrator drugs itraconazole and rifampicin. Predictions of the magnitude of change in exposure deviated at most 0.99- to 1.31-fold from clinical trial results for inhibition with itraconazole, whereas exposure predictions for the induction with rifampicin were less accurate, with deviations of 0.22- to 0.48-fold. Results for the early prediction of DDIs and their clinical impact appear promising for CYP3A4 inhibition, but validation with more victim and perpetrator drugs is essential to evaluate the performance of the new method. SIGNIFICANCE STATEMENT: The described method offers an alternative for the early detection and assessment of potential clinical impact of CYP3A4-related drug-drug interactions. The model was able to adequately describe the inhibition of CYP3A4 metabolism and the subsequent magnitude of change in exposure. However, it was unable to accurately predict the magnitude of change in exposure of victim drugs in combination with an inducer.

Addressing Drug-Drug Interaction Knowledge Gaps at the Time of Approval: An Analysis of FDA Postmarketing Requirements and Commitments from 2009 to 2023.

It has become increasingly common for patients to rely on the use of multiple prescription medications. The management of polypharmacy requires careful consideration for how drugs are metabolized and their potential for interaction with other drugs. Drug-drug interaction (DDI) assessments are typically performed in a stepwise manner during drug development, though knowledge gaps can exist at the time of approval. The US Food and Drug Administration can establish postmarketing requirements (PMRs) or postmarketing commitments (PMCs) to address these knowledge gaps. In this study, we systematically evaluated PMRs and PMCs established to new molecular entities (NMEs) at the time of initial approval between 2009 and 2023, for the assessment of pharmacokinetics-based DDIs (i.e., drug metabolizing enzyme- and transporter-related DDIs). We found that 22% of NMEs had at least one DDI-related PMR or PMC, with a total of 263 that were pharmacokinetic based. Of these, 67% were for the assessment of drug metabolizing enzymes, which were established most frequently for their evaluation as a substrate, and 28% for transporters, which were established most frequently for their evaluation as an inhibitor. The 89% of PMRs and PMCs that were considered fulfilled had a revision to the United States prescribing information, the majority of which resulted in updated new instructions for use. This study highlights the value in conducting PMRs and PMCs early in the drug development process allowing broad patient inclusion at the time of initial drug approval.

Assessment of drug-drug interactions among patients with hematologic malignancy: A clinical pharmacist-led study.

Patients with hematologic malignancies often receive multiple medications, leading to potential drug-drug interactions (DDIs). Identifying and managing these DDIs is crucial for ensuring patient safety and effective care. This study aimed to identify and describe DDIs and associated factors in hematologic malignancy patients. This prospective interventional study was conducted at a referral center and included hospitalized patients with hematologic malignancies who were receiving at least four concurrent medications. A pharmacist initially compiled a comprehensive list of all medications through patient interviews and medication reviews, and subsequently, identified and categorized potential DDIs using the Lexi-interact® and Micromedex® databases. The clinical pharmacist then evaluated the clinical impact of the identified DDIs in every individual patient and provided appropriate interventions to resolve them. A total of 200 patients met the inclusion criteria for the study, with 1281 DDIs identified across 337 distinct types. The majority of identified DDIs exhibited major severity (52.1%) and pharmacokinetic mechanisms (50.3%), with an unspecified onset (79.4%) and fair evidence (67%). Of the identified DDIs, 81.1% were considered clinically significant, prompting 1059 pharmacotherapy interventions by the clinical pharmacist. Additionally, a significant relationship was observed between the number of drugs used during hospitalization and the occurrence of DDIs (P < 0.001, r = 0.633). DDIs are highly prevalent among hospitalized patients with hematologic malignancies, with their occurrence increasing alongside the number of medications administrated. The intervention of a clinical pharmacist is crucial to evaluate the clinical impact of these DDIs and implement effective interventions for their management.

Assessment of drug-drug interaction of dapagliflozin with LCZ696 based on an LC-MS/MS method.

The co-administration of dapagliflozin (DPF) and sacubitril/valsartan (LCZ696) has emerged as a promising therapeutic approach for managing heart failure. Given that DPF and LCZ696 are substrates for P-glycoprotein, there is a plausible potential for drug-drug interactions when administered concomitantly. To investigate the pharmacokinetic changes when these drugs are co-administered, we have established and validated a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method capable of simultaneously detecting DPF, LBQ657 (the active metabolite of sacubitril) and valsartan in rat plasma. This method has demonstrated selectivity, sensitivity, and accuracy. Drug-drug interactions were examined by the LC-MS/MS method. The mechanisms were investigated using everted intestinal sac models and Caco-2 cells. The results showed that DPF significantly increased the area under the curve (AUC(0-t)) (3,563.3 ± 651.7 vs. 7,146.5 ± 1,714.9h μg/L) of LBQ657 (the active metabolite of sacubitril) and the AUC(0-t) (24,022.4 ± 6,774.3 vs. 55,728.3 ± 32,446.3h μg/L) of valsartan after oral co-administration. Dapagliflozin significantly increased the amount of LBQ657 and valsartan in intestinal sacs by 1- and 1.25-fold at 2.25 h. Caco-2 cell uptake studies confirmed that P-glycoprotein is the transporter involved in this interaction. This finding enhances the understanding of drug-drug interactions in the treatment of heart failure and provides a guidence for clinical therapy.

Drug-drug interactions of icenticaftor (QBW251) with a 5-probe cytochrome P450 cocktail and oral contraceptives.

A drug-drug interaction (DDI) study was conducted to evaluate the effect of icenticaftor (QBW251) on the pharmacokinetics (PK) of a 5-probe cytochrome P450 (CYP) substrate cocktail, guided by invitro studies in human hepatocytes and liver microsomes. Another DDI study investigated the effect of icenticaftor on the PK and pharmacodynamics (PD) of a monophasic oral contraceptive (OC) containing ethinyl estradiol (EE) and levonorgestrel (LVG) in premenopausal healthy female subjects. The static-mechanistic DDI assessment indicated that icenticaftor may moderately induce the metabolic clearance of co-medications metabolized by CYP3A4 (area under the concentration-time curve [AUC] ratio: 0.47) and potentially CYP2C; icenticaftor may also weakly inhibit the metabolic clearance of co-medications metabolized by CYP1A2 and CYP3A4 (AUC ratio: 1.35 and 1.86, respectively) and moderately inhibit CYP2B6 (AUC ratio: 2.11). In the CYP substrate cocktail DDI study, icenticaftor 300 mg twice daily (b.i.d.) moderately inhibited CYP1A2 (AUC ratio: 3.35) and CYP2C19 (AUC ratio: 2.70). As expected from the results of the invitro studies, weak induction was observed for CYP3A4 (AUC ratio: 0.51) and CYP2C8 (AUC ratio: 0.66). In the OC DDI study, co-administration of icenticaftor 450 mg b.i.d. with monophasic OC containing 30-μg EE and 150-μg LVG once daily reduced the plasma exposure of both components by approximately 50% and led to increased levels of follicle-stimulating hormone and luteinizing hormone. These results provide valuable guidance for the use of icenticaftor in patients taking concomitant medications that are substrates of CYP enzymes or patients using OCs.

Drug-Drug Interactions in Nosocomial Infections: An Updated Review for Clinicians.

Prevention, assessment, and identification of drug-drug interactions (DDIs) represent a challenge for healthcare professionals, especially in nosocomial settings. This narrative review aims to provide a thorough assessment of the most clinically significant DDIs for antibiotics used in healthcare-associated infections. Complex poly-pharmaceutical regimens, targeting multiple pathogens or targeting one pathogen in the presence of another comorbidity, have an increased predisposition to result in life-threatening DDIs. Recognising, assessing, and limiting DDIs in nosocomial infections offers promising opportunities for improving health outcomes. The objective of this review is to provide clinicians with practical advice to prevent or mitigate DDIs, with the aim of increasing the safety and effectiveness of therapy. DDI management is of significant importance for individualising therapy according to the patient, disease status, and associated comorbidities.

Pharmacokinetics and Bioavailability Study of a Novel Smoothened Inhibitor TPB15 for Treatment of Triple-Negative Breast Cancer in Rats by High Performance Liquid Chromatography-Mass Spectrometry.

Smoothened (SMO), a key component of the hedgehog signaling pathway, represents a therapeutic target for triple negative breast cancer(TNBC), yet the chemotherapy response rate in TNBC patients is only 40-50%, underscoring the urgent need for the development of novel drugs to effectively treat this condition. The novel compound TPB15, an SMO inhibitor derived from [1,2,4] triazolo [4,3-α] pyridines, demonstrated superior anti-TNBC activity and lower toxicity compared to the first SMO inhibitor vismodegib in both in vitro and in vivo. However, the compound's pharmacokinetic properties remain unclear. The present work aims to develop a simple HPLC-MS/MS method to profile the pharmacokinetics and bioavailability of TPB15 in rats as a ground work for further clinical research. Separation was performed on an Agilent ZORBAX StableBond C18 column by gradient elution using acetonitrile and 0.1% formic acid as mobile phase at a flow rate of 0.3mL/min. Multiple reaction monitoring(MRM) in positive mode with the transitions of m/z 454.2 → 100.0, 248.1 → 121.1 was employed to determine TPB15 and internal standard tinidazole, respectively. The specificity, intra- and inter- day precision and accuracy, extraction recovery, stability, matrix effect, dilution integrity and carryover of the method was validated. The pharmacokinetics and bioavailability studyof TPB15 were carried out on rats through intravenous injection at the dose of 5 mg/kg and oral gavage at the dose of 25 mg/kg, and the pharmacokinetics parameters were calculated by the non-compartment analysis using the pharmacokinetics software DAS 2.1.1. The values of specificity, intra- and inter- day precision and accuracy, extraction recovery, stability, matrix effect, dilution integrity and carryover satisfied the acceptable limits. The lower limit of quantification of this method was 10 ng/mL with a linear range of 10-2000ng/mL. The validated method was then applied to pharmacokinetics and bioavailability studies in rat by dosing with gavage (25mg/kg) and intravenous injection(5mg/kg), and the oral bioavailability of TBP15 in rat was calculated as 16.4 ± 3.5%. The pharmacokinetic parameters were calculated as following: maximum of plasma concentration (Cmax) (PO: 2787.17 ± 279.45µg/L), Time to maximum plasma concentration (Tmax) (PO: 4.20 ± 0.90 h), the area under the concentration-time curve 0 to time (AUC0-t) (PO: 17,373.03 ± 2585.18 ng/mL·h, IV: 21,129.79 ± 3360.84ng/mL·h), the area under the concentration-time curve 0 to infinity (AUC0-∞) (PO: 17,443.85 ± 2597.63 ng/mL·h, IV: 17,443.85 ± 2597.63ng/mL·h), terminal elimination half-life (t1/2) (PO: 7.26 ± 2.16h, IV: 4.78 ± 1.09h). TPB15, a promising candidate for treating TNBC, hasdemonstrated outstanding efficacy and safety in vitro and in vivo. This study established a simple, sensitive, and rapid HPLC-MS/MS bioanalytical method, developed and validated in accordance with FDA and EMA guidelines, for conducting pharmacokinetic and bioavailability studies of TPB15. The results revealed a favorable pharmacokinetic profile owing to its long t1/2. Nevertheless, the next phase of research should include formulation screening to enhance bioavailability, as well as clinical trials, metabolism pathway analysis, and assessment of potential drug-drug interactions.

Assessment of potential drug-drug interactions in hospitalized patients with infectious diseases: an experience from a secondary care hospital

Background Polypharmacy is common among hospitalized patients with infectious infections owing to comorbidities or concomitant illnesses. This raises the likelihood of drug-drug interactions and creates uncertainty for healthcare providers. This study aimed to assess the potential drug-drug interactions (pDDIs) among hospitalized patients with infectious diseases in a secondary care hospital. Methods A prospective observational study was conducted in the internal medicine ward for six months after the ethics committee’s approval. Data were collected from patient case records, and prescriptions were screened for pDDIs from a portable electronic physician information database (PEPID) resource analyzed using SPSS, version 27.0. Results In total, 148 patient case records were analyzed, and 549 pDDIs were identified, with 66.8% having at least one or more DDIs. The mean number of drug interactions was 3.70 ± 4.58 per prescription. The most frequently encountered drug interactions were drug combinations such as bisoprolol with atorvastatin and aspirin with tazobactam/piperacillin. Bivariate analysis showed that age, comorbidities, length of hospital stay, and the number of drugs prescribed were risk factors associated with DDIs (p&lt;0.05). In the multiple binary logistic regression analysis, DDIs were significantly associated with comorbidities and the number of prescribed medications (p&lt;0.0001). Conclusions This study observed the prevalence of DDIs in hospitalized patients with infectious diseases of ‘moderate’ severity. Prescription screening using a drug information database assists in early identification and prevention of DDIs, enhancing drug safety and quality of patient-centered care.

Clinical Trial Data-Driven Risk Assessment of Drug-Drug Interactions: A Rapid and Accurate Decision-Making Tool.

In clinical practice, the vast array of potential drug combinations necessitates swift and accurate assessments of pharmacokinetic drug-drug interactions (DDIs), along with recommendations for adjustments. Current methodologies for clinical DDI evaluations primarily rely on basic extrapolations from clinical trial data. However, these methods are limited in accuracy owing to their lack of a comprehensive consideration of various critical factors, including the inhibitory potency, dosage, and type of the inhibitor, as well as the metabolic fraction and intestinal availability of the substrate. This study aims to propose an efficient and accurate clinical pharmacokinetic-mediated DDI assessment tool, which comprehensively considers the effects of inhibitory potency and dosage of inhibitors, intestinal availability and fraction metabolized of substrates on DDI outcomes. This study focuses on DDIs caused by cytochrome P450 3A4 enzyme inhibition, utilizing extensive clinical trial data to establish a methodology to calculate the metabolic fraction and intestinal availability for substrates, as well as the concentration and inhibitory potency for inhibitors ( or ). These parameters were then used to predict the outcomes of DDIs involving 33 substrates and 20 inhibitors. We also defined the risk index for substrates and the potency index for inhibitors to establish a clinical DDI risk scale. The training set for parameter calculation consisted of 73 clinical trials. The validation set comprised 89 clinical DDI trials involving 53 drugs. which was used to evaluate the reliability of in vivo values of and , the accuracy of DDI predictions, and the false-negative rate of risk scale. First, the reliability of the in vivo and values calculated in this study was assessed using a basic static model. Compared with values obtained from other methods, this study values showed a lower geometric mean fold error and root mean square error. Additionally, incorporating these values into the physiologically based pharmacokinetic-DDI model facilitated a good fitting of the C-t curves when the substrate's metabolic enzymes are inhibited. Second, area under the curve ratio predictions of studied drugs were within a 1.5× margin of error in 81% of cases compared with clinical observations in the validation set. Last, the clinical DDI risk scale developed in this study predicted the actual risks in the validation set with only a 5.6% incidence of serious false negatives. This study offers a rapid and accurate approach for assessing the risk of pharmacokinetic-mediated DDIs in clinical practice, providing a foundation for rational combination drug use and dosage adjustments.

Effect of Erdafitinib on the Pharmacokinetics of Midazolam and Metformin in Patients With Advanced Solid Tumors Harboring FGFR Gene Alterations.

Erdafitinib, an oral pan-FGFR inhibitor, is used in locally advanced or metastatic urothelial carcinoma for adults with FGFR3 genetic alterations and whose disease progressed following prior systemic therapy. This drug-drug interaction substudy evaluated the effect of erdafitinib on the pharmacokinetics of midazolam (cytochrome P450 3A4 substrate), and metformin (organic cation transporter 2 substrate). Twenty-five patients with advanced solid tumors harboring FGFR gene alterations received pretreatment with single doses of midazolam and metformin, followed by a daily dose of erdafitinib. Drug-drug interaction assessments were performed at erdafitinib steady state following coadministration of single doses of midazolam and metformin, respectively. Geometric mean ratios for maximum plasma concentration and area under the plasma concentration-time curve (AUC) from time 0 to the last measurable concentration, and AUC from time 0 to infinity were estimated using linear mixed-effects models (90% confidence interval within 80%-125% indicated no interaction). The 90% confidence intervals of geometric mean ratios for maximum plasma concentration, AUC from time 0 to the last measurable concentration, and AUC from time 0 to infinity of midazolam (86.3%, 88.5%, and 82.1%), 1-OH midazolam (99.8%, 97.4%, and 101.5%), and metformin (108.7%, 119.0%, and 113.9%) were either contained or slightly outside the 80%-125% interval and not considered clinically meaningful. Adverse events were consistent with the known erdafitinib safety profile; no new safety signals emerged. Thus, repeated dosing of erdafitinib had no clinically meaningful effect on the pharmacokinetics of midazolam or metformin.

Polypharmacy, anticholinergic burden and drug-drug interaction assessment in people with four-class-resistant HIV: data from the PRESTIGIO registry.

To evaluate polypharmacy, anticholinergic burden (ACB) and drug-drug interactions (DDIs) in people with four-class-resistant HIV (4DR-PWH). We performed a cross-sectional study, including 4DR-PWH from the PRESTIGIO Registry taking at least one non-antiretroviral drug. Polypharmacy was defined as taking five or more non-antiretroviral drugs. ACB was calculated using the ACB scale: 0 = no AC effect, 1-2 = low/moderate risk, ≥3 = high AC risk. Participants' characteristics by ACB score were compared using the Kruskal-Wallis test, and Spearman's correlation coefficient was used to assess linear relationships. DDIs were evaluated using the Liverpool database. Overall, 172 4DR-PLWH were evaluated: 75.6% males, median age 49.9 years (IQR = 45.6-56), 62 (27.1%) on polypharmacy, 124 (72.1%) using a boosting agent and 72 (41.8%) with four or more antiretrovirals. Based on ACB, 128 (74.45%), 33 (19.2%) and 11 (6.4%) had a no, low/moderate and high AC risk, respectively. The most common AC drugs were β-blockers (12.2%), diuretics (8.7%) and antidepressants (8.7%). The high ACB was significantly related to the number of drugs/person (r = 0.33, P < 0.0001) and the number of clinical events (r = 0.222, P = 0.004). Overall, 258 DDIs were found between antiretrovirals and co-medications in 115 (66.8%) PWH, and 14 (8.1%) PWH received contraindicated drug combinations. In 4DR-PWH, polypharmacy, DDIs and the proportion of people with moderate/high AC burden were high. In 4DR-PWH undetectability achievement and maintenance is the priority and use of boosted PIs is common. A strict collaboration (infectious diseases specialists, virologists, pharmacologists) is needed to limit the risk of ACB and DDIs and to explore the advantages of new antiretrovirals.

1β-Hydroxydeoxycholic Acid as an Endogenous Biomarker in Human Plasma for Assessment of CYP3A Clinical Drug-Drug Interaction Potential.

4β-Hydroxycholesterol (4β-HC) in plasma has been used as a biomarker to assess CYP3A drug-drug interaction (DDI) potential during drug development. However, due to the long half-life and narrow dynamic range of 4β-HC, its use has been limited to the identification of CYP3A inducers, but not CYP3A inhibitors. The formation of 1β-hydroxydeoxycholic acid (1β-OH DCA) from deoxycholic acid (DCA) is mediated by CYP3A, thus 1β-OH DCA can potentially serve as an alternative to 4β-HC for assessment of CYP3A DDI potential. To study this feasibility, we developed a sensitive liquid chromatography-tandem mass spectrometry method for the simultaneous quantitation of 1β-OH DCA and its glycine and taurine conjugates in human plasma with the lower limit of quantitation of 50 pg/ml, which enabled the quantitation of basal levels and further reduction. The method was applied to a DDI study to assess how 1β-OH DCA and its glycine and taurine conjugates would respond to CYP3A induction or inhibition. Rifampin induction resulted in an increase of 1β-OH DCA and its conjugates in plasma, with 6.8-, 7.8-, 8.3-, and 10.3-fold increases of area under the curve from the time of dosing to the last measurable concentration (AUCLST), area under the curve from the time of dosing to 24 hours (AUC24h), C max, and mean concentrations for total 1β-OH DCA (total of all three forms), respectively. Importantly, inhibition with itraconazole resulted in notable reduction of these biomarkers, with 84%, 85%, 82%, and 81% reductions of AUCLST, AUC24h, C max, and mean concentrations for total 1β-OH DCA, respectively. These preliminary data demonstrate for the first time that total 1β-OH DCA in plasma has the potential to serve as a biomarker for CYP3A DDI assessment in early clinical development and may provide key advantages over 4β-HC. SIGNIFICANCE STATEMENT: The authors have reported the use of total 1β-hydroxydeoxycholic acid (1β-OH DCA) (sum of 1β-OH DCA and its glycine and taurine conjugates) plasma exposure as a biomarker for CYP3A activity. Itraconazole inhibition led to an 81%-85% decrease of total 1β-OH DCA plasma exposures, whereas rifampin induction led to a 6.8- to 10.3-fold increase of total 1β-OH DCA plasma exposures. Using 1β-OH DCA exposures in plasma also provides the benefit of allowing pharmacokinetic and biomarker assessment using the same matrix.

Monitoring and safety assessment of drug-drug interactions in elderly patients

Most elderly patients have polymorbidity, have more frequent illnesses, and greater medical care needs. Each of the existing diseases requires constant drug therapy, thus increasing the risk of developing adverse drug reactions from the medications taken. It is also necessary to consider that elderly patients are more susceptible to the occurrence of serious adverse drug reactions and drug-drug interactions.

Expanding Role of Endogenous Biomarkers for Assessment of Transporter Activity in Drug Development: Current Applications and Future Horizon.

The evaluation of transporter-mediated drug-drug interactions (DDIs) during drug development and post-approval contributes to benefit-risk assessment and helps formulate clinical management strategies. The use of endogenous biomarkers, which are substrates of clinically relevant uptake and efflux transporters, to assess the transporter inhibitory potential of a drug has received widespread attention. Endogenous biomarkers, such as coproporphyrin (CP) I and III, have increased mechanistic understanding of complex DDIs. Other endogenous biomarkers are under evaluation, including, but not limited to, sulfated bile acids and 4-pyridoxic acid (PDA). The role of endogenous biomarkers has expanded beyond facilitating assessment of transporter-mediated DDIs and they have also been used to understand alterations in transporter activity in the setting of organ dysfunction and various disease states. We envision that endogenous biomarker-informed approaches will not only help to formulate a prudent and informed DDI assessment strategy but also facilitate quantitative predictions of changes in drug exposures in specific populations.

Pharmacokinetics-Pharmacodynamics Modeling for Evaluating Drug-Drug Interactions in Polypharmacy: Development and Challenges.

Polypharmacy is commonly employed in clinical settings. The potential risks of drug-drug interactions (DDIs) can compromise efficacy and pose serious health hazards. Integrating pharmacokinetics (PK) and pharmacodynamics (PD) models into DDIs research provides a reliable method for evaluating and optimizing drug regimens. With advancements in our comprehension of both individual drug mechanisms and DDIs, conventional models have begun to evolve towards more detailed and precise directions, especially in terms of the simulation and analysis of physiological mechanisms. Selecting appropriate models is crucial for an accurate assessment of DDIs. This review details the theoretical frameworks and quantitative benchmarks of PK and PD modeling in DDI evaluation, highlighting the establishment of PK/PD modeling against a backdrop of complex DDIs and physiological conditions, and further showcases the potential of quantitative systems pharmacology (QSP) in this field. Furthermore, it explores the current advancements and challenges in DDI evaluation based on models, emphasizing the role of emerging in vitro detection systems, high-throughput screening technologies, and advanced computational resources in improving prediction accuracy.

Thinking Three-Dimensionally: A Self- and Externally-Controlled Approach to Screening for Drug-Drug-Drug Interactions Among High-Risk Populations.

The global rise in polypharmacy has increased both the necessity and complexity of drug-drug interaction (DDI) assessments, given the growing potential for interactions involving more than two drugs. Leveraging large-scale healthcare claims data, we piloted a semi-automated, high-throughput case-crossover-based approach for drug-drug-drug interaction (3DI) screening. Cases were direct-acting oral anticoagulant (DOAC) users with either a major bleeding event during ongoing dispensings for potentially interacting, enzyme-inhibiting antihypertensive drugs (AHDs) (Study 1), or a thromboembolic event during ongoing dispensings for potentially interacting, enzyme-inducing antiseizure medications (ASMs) (Study 2). 3DI detection was based on screening for additional drug exposures that served as acute outcome triggers. To mitigate direct effects and confounding by concomitant drugs, self-controlled estimates were adjusted using negative cases (external "control" DOAC users with the same outcomes but co-dispensings for non-interacting AHDs or ASMs). Signal thresholds were set based on P-values and false discovery rate q-values to address multiple comparisons. Study 1: 285 drugs were examined among 3,306 episodes. Self-controlled assessments with q-value thresholds yielded 9 3DI signals (cases) and 40 DDI signals (negative cases). External adjustment generated 10 3DI signals from the P-value threshold and no signals from the q-value threshold. Study 2: 126 drugs were examined among 604 episodes. Assessments with P-value thresholds yielded 3 3DI and 26 DDI signals following self-control, as well as 4 3DI signals following adjustment. No 3DI signals met the q-value threshold. The presented self- and externally-controlled approach aimed to advance paradigms for real-world higher order drug interaction screening among high-susceptibility populations with pre-existent DDI risk.

Utilising Endogenous Biomarkers in Drug Development to Streamline the Assessment of Drug-Drug Interactions Mediated by Renal Transporters: A Pharmaceutical Industry Perspective.

The renal secretion of many drugs is facilitated by membrane transporters, including organic cation transporter 2, multidrug and toxin extrusion protein 1/2-K and organic anion transporters 1 and 3. Inhibition of these transporters can reduce renal excretion of drugs and thereby pose a safety risk. Assessing the risk of inhibition of these membrane transporters by investigational drugs remains a key focus in the evaluation of drug-drug interactions (DDIs). Current methods to predict DDI risk are based on generating in vitro data followed by a clinical assessment using a recommended exogenous probe substrate for the individual drug transporter. More recently, monitoring plasma-based and urine-based endogenous biomarkers to predict transporter-mediated DDIs in early phase I studies represents a promising approach to facilitate, improve and potentially avoid conventional clinical DDI studies. This perspective reviews the evidence for use of these endogenous biomarkers in the assessment of renal transporter-mediated DDI, evaluates how endogenous biomarkers may help to expand the DDI assessment toolkit and offers some potential knowledge gaps. A conceptual framework for assessment that may complement the current paradigm of predicting the potential for renal transporter-mediated DDIs is outlined.

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.

Abstract PO5-18-11: Use of DiviTum®TKa as a biomarker assay for CDK4/6 inhibitor medication compliance and drug-drug interaction assessment in ER/PR positive metastatic breast cancer

Abstract Background: CDK4/6 inhibitor (CDK4/6i) with endocrine therapy (ET) is standard first line (1L) treatment for metastatic hormone positive (ER/PR+) HER2- negative breast cancer (mBC). Some patients have progression free survival (PFS) of 2 years or more whereas some patients have very short PFS on these medications. Thymidine kinase is a biomarker that reflects cell proliferation. DiviTum®TKa is an assay that was cleared by the FDA in July 2022 based on data that showed that thymidine kinase activity (TKa) value of &amp;lt; 250 DiviTum units (DuA) is associated with the decreased likelihood of disease progression within 30 days or 60 days post testing. The purpose of this study is to assess the use of TKa as a biomarker for identifying potential medical compliance issues and drug-drug interactions (DDI) in patients on CDK4/6i and to evaluate whether counseling and optimization of concurrent medications will lead to lower TKa levels in subsequent treatment cycles and possibly longer PFS. Trial design: This is a pilot study to assess whether TKa can serve as a biomarker to identify patients with a suboptimal reduction in tumor cell proliferation caused by a potential CDK4/6i compliance or DDI issue. We will also assess whether correction of the issue can improve suppression of TKa levels in ER/PR+ mBC patients on 1L CDK4/6i and ET. For screening, we are utilizing a novel automated real-time data driven cohort identification system designed by Dr. Guannan Gong at Yale. Patients will be treated and monitored as per standard of care. An aliquot of serum obtained during routine blood draws will be sent to a CLIA-certified Biovica lab for TKa testing. TKa results will be returned in real time. Patients with a detectable TKa baseline level (above 145 DuA), followed by a sustained TKa level (&amp;gt;145 DuA) at cycle 1 D15 and D28 will be assessed for medication compliance and DDI. TKa levels will be repeated at subsequent standard of care blood draws to assess if the intervention affected TKa levels. Patients will be followed for 2 years. Eligibility criteria: Participants must have ER+ mBC and starting 1L CDK4/6i + ET and be previously CDK4/6i -naïve. Specific aims: Primary objective: to estimate the rate of improvement in CDK4/6i response (change in TKa levels) after counseling for medication compliance and adjustment of potential deleterious DDIs. Secondary objectives: 1) estimate the rate of sub-optimal CDK 4/6i response in cycle 1 of treatment. 2) Compare clinical benefit rate (CBR) in patients with sub-optimal and optimal CDK4/6i response after both cycles 1 and cycle 3. 3) Compare PFS in patients with sub-optimal and optimal CDK 4/6i response after both cycles 1 and cycle 3. 4) Assess CDK4/6i response via TKa levels upon CDK4/6i dose reductions or changes in CDK4/6i regimens. 5) Compare CDK4/6i response profiles across the three CDK 4/6i among different patients and within the same patients if CDK4/6i is changed throughout treatment course. 6) Correlate TKa levels with tumor marker levels. 7) Assess plasma concentrations of CDK4/6i in patients with suboptimal TKa levels. Statistical methods: For binary endpoints measured at a given time point, we will estimate the two-sided 95% exact CI using Clopper-Pearson method. With a sample size of 120 patients, assuming 20% drop out rate, we predict that about 15% of patients will have suboptimal CDK4/6i response, estimating a 95% CI of 0.086-0.235 in the first cycle. Comparisons between paired outcomes will be completed using the paired t-test or Wilcoxon signed-rank test for continuous parameters of interest, or McNemar’s Chi-square test for categorical parameters of interest. Time to event endpoints will be estimated using Kaplan-Meier method. Present accrual and target accrual: This trial has just been activated. Target accrual is 120 patients. Contact information for people with a specific interest in the trial: Mariya.Rozenblit@yale.edu Citation Format: Mariya Rozenblit, Adriana Kahn, Guannan Gong, Amy Williams, Lajos Pusztai. Use of DiviTum®TKa as a biomarker assay for CDK4/6 inhibitor medication compliance and drug-drug interaction assessment in ER/PR positive metastatic breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-18-11.

Dose Optimization Informed by PBPK Modeling: State-of-the Art and Future.

Model-informed drug development (MIDD) is a powerful quantitative approach that plays an integral role in drug development and regulatory review. While applied throughout the life cycle of the development of new drugs, a key application of MIDD is to inform clinical trial design including dose selection and optimization. To date, physiologically-based pharmacokinetic (PBPK) modeling, an established component of the MIDD toolkit, has mainly been used for assessment of drug-drug interactions (DDIs) and consequential dose adjustments in regulatory submissions. As a result of recent scientific advances and growing confidence in the utility of the approach, PBPK models are being increasingly applied to provide dose recommendations for subjects with differing ages, genetics, and disease states. In this review, we present our perspective on the current landscape of regulatory acceptance of PBPK applications supported by relevant case studies. We also discuss the recent progress and future challenges associated with expanding the utility of PBPK models into emerging areas for regulatory decision making, especially dose optimization in highly vulnerable and understudied populations and facilitating diversity in clinical trials.