Evaluation Of Drug-drug Interactions Research Articles

Clinical Evaluation of Drug-Drug Interactions with Obeldesivir, an Orally Administered Antiviral Agent.

Obeldesivir is an oral nucleoside analog prodrug inhibitor of SARS-CoV-2 RNA-dependent RNA polymerase and other viral polymerases. Here, two Phase I studies evaluated potential drug-drug interactions between obeldesivir and substrates or inhibitors of cytochrome P450 and drug transporters in healthy participants. When obeldesivir was tested as a precipitant, pharmacokinetic parameter point estimates for midazolam (CYP3A4 inhibition/induction), caffeine (CYP1A2 inhibition), and metformin (organic cation transporter 1 inhibition) exposures were within 80-125% no-effect bounds representing the interval within which a systemic exposure change does not warrant clinical action based on EMA/FDA guidance. Dabigatran (P-glycoprotein substrate) and pitavastatin (organic anion transporting polypeptide 1B1/1B3 substrate) exposures decreased by approximately 25% and 30%, respectively, with obeldesivir coadministration; these were considered not clinically relevant, as these exposure changes are not associated with dose changes or precautions in the US prescribing information for these drugs. When obeldesivir was evaluated as an object, exposures of GS-441524, the parent nucleoside monophosphate metabolite of obeldesivir, were within the 80-125% no-effect bounds for ritonavir (P-glycoprotein inhibition) and cyclosporin A (breast cancer resistance protein inhibition) coadministration. Famotidine (gastric acid suppression) coadministration decreased GS-441524 exposure by approximately 26%; this was within the range of exposures observed in previous Phase III studies and was considered not clinically relevant. Obeldesivir was well tolerated, and adverse events were mild to moderate. These findings indicate that obeldesivir has low potential for drug-drug interactions. Obeldesivir remains a promising treatment against a broad spectrum of viruses given its antiviral activity and favorable safety profile.

Evaluation of drug-drug interaction between rosuvastatin and tacrolimus and the risk of hepatic injury in rats.

Multimorbidity, therapeutic complexity, and polypharmacy, which greatly increases the risk of drug-drug interactions (DDIs) and adverse medical outcomes, have become important and growing challenges in clinical practice. Statins are frequently prescribed to manage post-transplant dyslipidemia and reduce overall cardiovascular risk in solid organ transplant recipients. This study aimed to determine whether rosuvastatin has significant DDIs with tacrolimus (the first-line immunosuppressant) and to evaluate the risk of hepatotoxicity associated with concomitant therapy. We first studied whether a rat model could be established to assess the magnitude of rosuvastatin-tacrolimus DDI. The liver function index and histopathological examination were performed to investigate the characteristics of hepatotoxicity in the presence and absence of DDI. The clinical DDI potential between rosuvastatin and tacrolimus was also explored. Single-dose intravenous administration of tacrolimus did not significantly affect the area under the plasma concentration-time curve (AUC0-∞), clearance (CL), and volume of distribution at steady-state (Vss) of rosuvastatin in rats, despite a 96.7% increase in the rosuvastatin maximum plasma concentration (P = 0.024). Multiple doses of intravenous tacrolimus had no effect on the systemic disposition of rosuvastatin, but significantly increased aspartate transaminase (AST) by 42.6% (P = 0.043). Multiple doses of intravenous tacrolimus and rosuvastatin significantly altered the disposition of rosuvastatin, reducing alanine aminotransferase(ALT) and AST by 38.3% (P = 0.040) and 31.6% (P = 0.019), respectively. Histological evaluation of the liver specimens revealed patterns of drug-induced liver injury in rats. At clinically relevant doses, tacrolimus was predicted to be unable to cause pharmacokinetic interactions with rosuvastatin through basic models. The concomitant administration of tacrolimus and rosuvastatin has a minor impact on rosuvastatin pharmacokinetics; however, mild hepatotoxicity has been observed in rats.

Cognitive and behavioral impact of antiseizure medications, neuromodulation, ketogenic diet, and surgery in lennox-gastaut syndrome: A comprehensive review.

Lennox-Gastaut syndrome (LGS) is a severe developmental and epileptic encephalopathy marked by drug-resistant seizures and profound cognitive and behavioral impairments, with nearly 95% of individuals affected by moderate to severe intellectual disability. This review comprehensively explores the cognitive and behavioral impacts of current treatment options for LGS, including antiseizure medications (ASMs), neuromodulation strategies, the ketogenic diet, and surgical interventions. Given the limited availability of LGS-specific data for several ASMs, the evidence base is supplemented with findings from general epilepsy populations and individuals with epilepsy and intellectual disabilities. The evidence reveals that ASMs exert varied cognitive and behavioral effects in LGS. Medications such as valproate, lamotrigine, cannabidiol, fenfluramine, levetiracetam, brivaracetam, felbamate, and rufinamide generally support cognitive stability, while topiramate and zonisamide are associated with cognitive challenges. Behavioral outcomes also vary: stability is observed with valproate, lamotrigine, rufinamide, cannabidiol, and fenfluramine, whereas medications like levetiracetam, perampanel, brivaracetam, clobazam, and zonisamide can increase aggression or irritability. Nonpharmacological therapies, particularly when they reduce seizure frequency, typically provide greater cognitive and behavioral stability, with some offering improvement. Early intervention-especially through surgical options-appears most beneficial for preserving cognitive function. Additionally, therapies such as the ketogenic diet and neuromodulation may provide independent cognitive benefits beyond seizure control. This review emphasizes the importance of personalized treatment strategies, integrating cognitive and behavioral evaluations in therapy selection. Key components include baseline cognitive and behavioral assessments, followed by regular follow-up evaluations, particularly after therapy changes. Consideration of minimizing ASM polytherapy, careful evaluation of drug-drug interactions, pharmacogenomic implications, and the need for therapeutic drug monitoring in cases of cognitive adverse effects is essential. Future research should focus on developing assessment tools tailored to the unique needs of individuals with LGS, utilizing connectivity measures to assess intervention impacts, and advancing precision therapeutics to improve cognitive and behavioral outcomes.

PBPK modelling for the evaluation of drug-drug interaction between meropenem and valproic acid.

The present study aimed to quantitatively investigate the potential drug-drug interaction (DDI) mechanism between meropenem (MEPM) and valproic acid (VPA). In the present study, we firstly developed a physiologically based pharmacokinetic (PBPK) model of MEPM and VPA. The verified PBPK model was then used to quantitatively investigate the potential DDI between MEPM and VPA. The effect of genetic polymorphisms of acylpeptide hydrolase (APEH) on DDI was also evaluated. In our simulation, the plasma concentration of hydrolysate of VPAG decreased to 63% when combined with MEPM. Total plasma concentration of VPA before carbapenem use was 53.61 mg/L, whereas it was 45.42 mg/L during carbapenem use. The inhibition of hydrolysis of VPAG by MEPM alone could not result in a rapid and substantial decrease in the plasma concentration of VPA. Parameter sensitivity analysis showed that the changes of absorption played an important role in the maximum plasma concentration (Cmax) of VPA, whereas area under the plasma concentration-time profile (AUC) was more susceptible to elimination changes. In addition, a decrease in APEH activity had little impact on the plasma pharmacokinetics of VPA. The DDI between MEPM and VPA might be a comprehensive result of multiple factors. On the basis of our simulation, interval medication of MEPM injection and VPA immediate release tablet at 4-6h timed interval was recommended, or intravenous administration of VPA solution was preferred when combination regimen was necessary in a clinical setting.

Evaluation of drug-drug interactions of a novel potent FLT3 inhibitor SKLB1028 in healthy subjects.

SKLB1028 is a novel multi-target protein kinase inhibitor under investigation for the treatment of FLT3-ITD mutated acute myeloid leukemia. Based on the preclinical characterization of SKLB1028 metabolism, three drug-drug interaction clinical studies were performed to investigate the effects of itraconazole, rifampin (CYP3A4 inhibitor and inducer, respectively), and gemfibrozil (CYP2C8 inhibitor) on the metabolism of SKLB1028. Fourteen healthy Chinese male subjects were enrolled in each study. In Study 1, subjects were administered a single dose of SKLB1028 (100 mg on days 1 and 11) and multiple doses of itraconazole (200 mg twice daily on day 8 and 200 mg once daily from days 9 to 18). Itraconazole was given with a loading dose on Day 8 and the total administration of itraconazole was 11 days. In Study 2, subjects were administered a single dose of SKLB1028 (100 mg on days 1 and 12) and multiple doses of gemfibrozil (600 mg twice daily from days 8 to 19). In Study 3, subjects were administered a single dose of SKLB1028 (150 mg on days 1 and 15) and multiple doses of rifampin (600 mg once daily from day 8 to 22). Itraconazole increased the AUC and Cmax of SKLB1028 by approximately 28% and 41%, respectively. Compared to the single drug, co-administration with gemfibrozil increased the AUC of SKLB1028 by ~26% and the Cmax by ~21%. Co-administration with rifampin reduced the AUC of SKLB1028 by ~30%, while the Cmax did not change significantly. All treatments were well tolerated in all three studies.

Evaluating the capability of ChatGPT in predicting drug-drug interactions: Real-world evidence using hospitalized patient data.

Drug-drug interactions (DDIs) present a significant health burden, compounded by clinician time constraints and poor patient health literacy. We assessed the ability of ChatGPT (generative artificial intelligence-based large language model) to predict DDIs in a real-world setting. Demographics, diagnoses and prescribed medicines for 120 hospitalized patients were input through three standardized prompts to ChatGPT version 3.5 and compared against pharmacist DDI evaluation to estimate diagnostic accuracy. Area under receiver operating characteristic and inter-rater reliability (Cohen's and Fleiss' kappa coefficients) were calculated. ChatGPT's responses differed based on prompt wording style, with higher sensitivity for prompts mentioning 'drug interaction'. Confusion matrices displayed low true positive and high true negative rates, and there was minimal agreement between ChatGPT and pharmacists (Cohen's kappa values 0.077-0.143). Low sensitivity values suggest a lack of success in identifying DDIs by ChatGPT, and further development is required before it can reliably assess potential DDIs in real-world scenarios.

Utility of Biomarker-Informed Drug Interaction Evaluation in Drug Development and Regulatory Decision Making.

The measurement of endogenous biomarkers in plasma and urine before and after administration of an investigational drug in a clinical study may provide an early indication of its drug-drug interaction (DDI) potential via a specific pathway. In the first international harmonized guideline on drug interaction studies, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M12, endogenous biomarkers have been recognized as an emerging approach in the transporter- and enzyme-based DDI risk assessment. Clinical Pharmacology Roundtable Conference 2024 held at Pharmaceuticals and Medical Devices Agency (PMDA) brought together experts from regulatory agencies, academia, and industries to discuss potential advantages and challenges of the biomarkers approach in drug development and regulatory decision making. This meeting report facilitates stakeholders involved in drug development in better understanding the utility of biomarker approaches and promotes early implementation of biomarker-informed DDI evaluation in regulatory use.

Clinical impact of drug-drug interactions on abemaciclib in the real-world experience of AB-ITALY study

Abemaciclib demonstrated clinical benefit in women affected by HR+/HER2− advanced breast cancer (aBC). Drug-drug interactions (DDIs) can lead to reduced treatment efficacy or increased toxicity. This retro-prospective study aimed to evaluate outcomes, DDIs’ impact, and toxicities of abemaciclib combined with endocrine therapy in a real-world setting. Patients from 12 referral Italian hospitals with HR+/HER2− aBC who received abemaciclib were included. Clinical data about comorbidities, concurrent medications, outcomes, and adverse events (AE) were collected. Drug-PIN® (Personalized Interactions Network) is a tool recognizing the role of multiple interactions between active and/or pro-drug forms combined with biochemical and demographic patient data. The software was used to define the Drug-PIN score and Drug-PIN tier (green, yellow, dark yellow, and red) for each patient. Univariate and multivariate analyses were performed to identify predictors of patients’ PFS or toxicity. One hundred seventy-three patients were included. 13% of patients had >75years. The overall response rate (ORR) was 63%. The general population’s median PFS (mPFS) was 22 months (mo), while mOS were not reached. Patients treated with abemaciclib in combination with AI and fulvestrant had a mPFS of 36 and 19 mo, respectively. The most common toxicities were diarrhea, asthenia, and neutropenia detected in 63%,49%, and 49% of patients. The number of concomitant medications and comorbidities were not associated with survival outcomes (22 vs 17 mo, p = 0.068, p = 0.99). Drug-PIN tier from dark yellow to red and Drug-PIN score >12 were associated with shorter PFS compared to no/low-risk DDIs and score <12 (15 vs 23, p = 0.005, p = 0.0017). Drug interaction was confirmed as an independent biomarker in a multivariate model (p = 0.02). No difference in any grade AE, severe toxicities, and diarrhea were detected among different age subgroups. No association was found between Drug-PIN score or Drug-PIN tier and overall toxicity (p = 0.44), severe AEs (p = 0.11), or drug reduction (p = 0.27). The efficacy and safety of abemaciclib plus ET were confirmed in a real-world setting, even in the elderly population and patients with comorbidities. Evaluation of DDIs with Drug-PIN appears to be an independent predictor of PFS.

Evaluation of Drug-Drug Interactions in Patients Admitting to the Ear Nose and Throat Clinic

Introduction: Drug interactions are effects that change the effect of the drug and therefore on the patient, mostly due to the use of drugs together at the same or recent times. Drug interactions can cause serious consequences such as the development of adverse events, ineffectiveness of the treatment, or mortality. Materials and Methods: The prescriptions of 106 patients who applied to the ear, nose, and throat outpatient clinic between January and December 2021 were examined retrospectively. Results: 106 prescriptions were included in the study. Twelve major, 19 moderate, and 9 minor interactions were detected in 106 prescriptions. Conclusion: Although there are relatively few interactions compared to studies conducted with other patient groups, interactions can affect the clinical status of the patient. More attention should be paid to drug interactions in these patients, especially physicians and pharmacists.

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.

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.

Evaluation of Drug-Drug Interaction Between Henagliflozin and Hydrochlorothiazide in Healthy Chinese Volunteers.

Henagliflozin is an original, selective sodium-glucose cotransporter 2 (SGLT2) inhibitor. Hydrochlorothiazide (HCTZ) is a common anti-hypertensive drug. This study aimed to evaluate the potential interaction between henagliflozin and HCTZ. This was a single-arm, open-label, multi-dose, three-period study that was conducted in healthy Chinese volunteers. Twelve subjects were treated in three periods, period 1: 25 mg HCTZ for four days, period 2: 10 mg henagliflozin for four days and period 3: 25 mg HCTZ + 10 mg henagliflozin for four days. Blood samples and urine samples were collected before and up to 24 hours after drug administrations on day 4, day 10 and day 14. The plasma concentrations of henagliflozin and HCTZ were analyzed using LC-MS/MS. The urine samples were collected for pharmacodynamic glucose and electrolyte analyses. Tolerability was also evaluated. The 90% CI of the ratio of geometric means (combination: monotherapy) for AUCτ,ss of henagliflozin and HCTZ was within the bioequivalence interval of 0.80-1.25. For henagliflozin, co-administration increased Css, max by 24.32% and the 90% CI of the GMR was (108.34%, 142.65%), and the 24-hour urine volume and glucose excretion decreased by 0.43% and 19.6%, respectively. For HCTZ, co-administration decreased Css, max by 19.41% and the 90% CI of the GMR was (71.60%, 90.72%), and the 24-hour urine volume and urinary calcium, potassium, phosphorus, chloride, and sodium excretion decreased by 11.7%, 20.8%, 11.8%, 11.9%, 22.0% and 15.5%, respectively. All subjects (12/12) reported adverse events (AEs), but the majority of theses AEs were mild and no serious AEs were reported. Although Css,max was affected by the combination of henagliflozin and HCTZ, there was no clinically meaningful safety interaction between them. Given these results, coadministration of HCTZ should not require any adaptation of henagliflozin dosing. ClinicalTrials.gov NCT06083116.

In vivo screening for toxicity-modulating drug interactions identifies antagonism that protects against ototoxicity in zebrafish.

Introduction: Ototoxicity is a debilitating side effect of over 150 medications with diverse mechanisms of action, many of which could be taken concurrently to treat multiple conditions. Approaches for preclinical evaluation of drug-drug interactions that might impact ototoxicity would facilitate design of safer multi-drug regimens and mitigate unsafe polypharmacy by flagging combinations that potentially cause adverse interactions for monitoring. They may also identify protective agents that antagonize ototoxic injury. Methods: To address this need, we have developed a novel workflow that we call Parallelized Evaluation of Protection and Injury for Toxicity Assessment (PEPITA), which empowers high-throughput, semi-automated quantification of ototoxicity and otoprotection in zebrafish larvae via microscopy. We used PEPITA and confocal microscopy to characterize in vivo the consequences of drug-drug interactions on ototoxic drug uptake and cellular damage of zebrafish lateral line hair cells. Results and discussion: By applying PEPITA to measure ototoxic drug interaction outcomes, we discovered antagonistic interactions between macrolide and aminoglycoside antibiotics that confer protection against aminoglycoside-induced damage to lateral line hair cells in zebrafish larvae. Co-administration of either azithromycin or erythromycin in zebrafish protected against damage from a broad panel of aminoglycosides, at least in part via inhibiting drug uptake into hair cells via a mechanism independent from hair cell mechanotransduction. Conversely, combining macrolides with aminoglycosides in bacterial inhibition assays does not show antagonism of antimicrobial efficacy. The proof-of-concept otoprotective antagonism suggests that combinatorial interventions can potentially be developed to protect against other forms of toxicity without hindering on-target drug efficacy.

Use of a Double-Transfected System to Predict hOCT2/hMATE1-Mediated Renal Drug-Drug Interactions.

Accurate predictions of renal drug-drug interactions (DDIs) mediated by the human organic cation transporter 2 (hOCT2) and multidrug and toxin extrusion proteins (hMATEs) remain challenging. Current DDI evaluation using plasma maximal unbound inhibitor concentrations (Imax,u) and IC50 values determined in single transporter-transfected cells frequently leads to false or overprediction especially for hMATE1. Emerging evidence suggests intracellular unbound inhibitor concentration may be more relevant for hMATE1 inhibition in vivo. However, determination of intrarenal inhibitor concentrations is impractical. Here, we explored the use of hOCT2/hMATE1 double-transfected Madin-Darby canine kidney (MDCK) cells as a new in vitro tool for DDI risk assessment. Our results showed that potent in vitro hMATE1 inhibitors (hydroxychloroquine, brigatinib, and famotidine) failed to inhibit metformin B-to-A flux in the double-transfected system. On the other side, the classic hOCT2/hMATE1 inhibitors, pyrimethamine and cimetidine, dose-dependently inhibited metformin apparent B-to-A permeability (Papp). The different behaviors of these hMATE1 inhibitors in the double-transfected system can be explained by their different ability to gain intracellular access either via passive diffusion or transporter-mediated uptake. A new parameter (IC50,flux) was proposed reflecting the inhibitor's potency on overall hOCT2/hMATE1-mediated tubular secretion. The IC50,flux values significantly differ from the IC50 values determined in single transporter-transfected cells. Importantly, the IC50,flux accurately predicted in vivo DDIs (within 2-fold) when used in a static model. Our data demonstrated that the IC50,flux approach circumvents the need to measure intracellular inhibitor concentrations and more accurately predicted hOCT2/hMATE1-mediated renal DDIs. This system represents a new approach that could be used for improved DDI assessment during drug development. SIGNIFICANCE STATEMENT: This study demonstrated that flux studies in double-transfected MDCK cells and the IC50,flux represents a better approach to assess in vivo DDI potential for the renal organic cation secretion system. This study highlights the importance of inhibitor intracellular accessibility for accurate prediction of hMATE1-mediated renal DDIs. This approach has the potential to identify in vitro hMATE1 inhibitors that are unlikely to result in in vivo DDIs, thus reducing the burden of unnecessary and costly clinical DDI investigations.

Evaluating drug-drug interaction risk associated with peptide analogues using advanced in vitro systems.

Drug-drug interaction (DDI) assessment of therapeutic peptides is an evolving area. The industry generally follows DDI guidelines for small molecules, but the translation of data generated with commonly used in vitro systems to in vivo is sparse. In the current study, we investigated the ability of advanced human hepatocyte in vitro systems namely HepatoPac, spheroids, and Liver-on-a-chip to assess potential changes in regulation of CYP1A2, CYP2B6, CYP3A4, SLCO1B1 and ABCC2 in the presence of selected therapeutic peptides, proteins, and small molecules. The peptide NN1177, a glucagon and GLP-1 receptor co-agonist, did not suppress mRNA expression or activity of CYP1A2, CYP2B6, and CYP3A4 in HepatoPac, spheroids, or Liver-on-a-chip; these findings were in contrast to the data obtained in sandwich cultured hepatocytes. No effect of NN1177 on SLCO1B1 and ABCC2 mRNA was observed in any of the complex systems. The induction magnitude differed across the systems (e.g., rifampicin induction of CYP3A4 mRNA ranged from 2.8-fold in spheroids to 81.2-fold in Liver-on-a-chip). Small molecules, obeticholic acid and abemaciclib, showed varying responses in HepatoPac, spheroids and Liver-on-a-chip, indicating a need for EC50 determinations to fully assess translatability data. HepatoPac, the most extensively investigated in this study (3 donors), showed high potential to investigate DDIs associated with CYP regulation by therapeutic peptides. Spheroids and Liver-on-a-chip were only assessed in one hepatocyte donor and further evaluations are required to confirm their potential. This study establishes an excellent foundation towards the establishment of more clinically-relevant in vitro tools for evaluation of potential DDIs with therapeutic peptides. Significance Statement At present, there are no guidelines for drug-drug interaction (DDI) assessment of therapeutic peptides. Existing in vitro methods recommended for assessing small molecule DDIs do not appear to translate well for peptide drugs, complicating drug development for these moieties. Here, we establish evidence that complex cellular systems have potential to be used as more clinically-relevant tools for the in vitro DDI evaluation of therapeutic peptides.

538. Clinical Evaluation of Drug-drug Interactions with Remdesivir

Abstract Background Remdesivir (RDV), a nucleotide prodrug approved for COVID-19, is a substrate and inhibitor of organic anion-transporter polyprotein (OATP) transporters and cytochrome P450 3A4 (CYP3A4) metabolizing enzyme based on in vitro data. Here, clinical studies of potential drug-drug interactions (DDIs) assessed RDV as a victim of OATP1B1/B3 inhibition and CYP3A induction and RDV as a perpetrator on OATP and CYP3A4. Methods Two Phase 1 open-label, fixed sequence studies were conducted in healthy participants (Table 1). Study 1 (n = 9) tested the effect of OATP1B1/1B3 inhibition (cyclosporin A, CsA) and CYP3A4 induction (carbamazepine, CBZ) on the pharmacokinetics (PK) of RDV. Study 2 (n = 19-22) tested the effect of RDV on the PK of substrates of OATP transporters (pitavastatin, PIT) and CYP3A4 (midazolam, MDZ). Plasma concentrations of RDV, its metabolites GS-704277 and GS-441524 (Study 1), and probe substrates (Study 2) were measured by validated mass spectrometry methods. PK parameters (AUCinf and Cmax) were estimated by noncompartmental analysis (Phoenix WinNonlin™). Treatments were compared with a parametric mixed-effects model with point estimates for geometric least-squares mean (GLSM) and 90% CI with pre-specified no-effect bounds. Safety was monitored throughout. Results In Study 1, coadministration of RDV with CsA resulted in 89%, 197%, and 3% increases in AUCinf for RDV, GS-704277, and GS-441524, respectively, whereas coadministration with CBZ did not impact AUCinf or Cmax for RDV or its metabolites. In Study 2, RDV did not impact AUCinf of PIT or MDZ, although mild increases in Cmax were observed for MDZ and 1’-OH-MDZ (29% and 25-39%, respectively). No deaths, serious adverse events (AEs), Grade 4 AEs, or AEs leading to discontinuation were reported. Most AEs were Grade 1 or 2 and consistent with known safety profiles.Table 1.Clinical RDV DDI Study Design CharacteristicsRDV, remdesivir; DDI, drug-drug interaction; CYP3A4, cytochrome P450 3A4; CBZ, carbamazepine; OATP, organic anion-transporter polyprotein; CsA, cyclosporin A; PIT, pitavastatin; MDZ, midazolam. Conclusion OATP 1B1/1B3 inhibition increased plasma PK exposures to RDV and GS-704277, but these increases remained within the range of exposures shown to be safe in Phase 3 studies and were not considered clinically relevant. CYP3A induction did not impact plasma PK exposures to RDV or metabolites. Low potential for RDV as a perpetrator of DDIs with CYP3A and OATP1B1/1B3 substrates was confirmed. For these DDIs, dose adjustments are not necessary with RDV. Disclosures Chi-Chi Peng, PhD, Gilead Sciences, Inc.: Employee|Gilead Sciences, Inc.: Stocks/Bonds Mark Shelton, PharmD, Certara, Inc.: Employee|Gilead Sciences, Inc.: Former Employee Anuja Raut, MS, MS, Gilead Sciences, Inc.: Employee|Gilead Sciences, Inc.: Stocks/Bonds Anna Kwan, BS, Gilead Sciences, Inc.: Employee|Gilead Sciences, Inc.: Stocks/Bonds Kanika Chhabra, MS, Gilead Sciences, Inc.: Employee|Gilead Sciences, Inc.: Stocks/Bonds Gong Shen, PhD, Gilead Sciences, Inc.: Employee|Gilead Sciences, Inc.: Stocks/Bonds Deqing Xiao, PhD, Gilead Sciences, Inc.: Employee|Gilead Sciences, Inc.: Stocks/Bonds Aryun Kim, PharmD, Gilead Sciences, Inc: Company employee|Gilead Sciences, Inc: Stocks/Bonds Sharline Madera, MD, PhD, Gilead Sciences, Inc.: Employee|Gilead Sciences, Inc.: Stocks/Bonds Robert H. Hyland, DPhil, Gilead Sciences, Inc.: Employee|Gilead Sciences, Inc.: Stocks/Bonds Helen Winter, PhD, Gilead Sciences, Inc.: Employee|Gilead Sciences, Inc.: Stocks/Bonds Rita Humeniuk, PhD, Gilead Sciences, Inc.: Employee|Gilead Sciences, Inc.: Stocks/Bonds

Impact of an embedded oncology clinical pharmacist in an outpatient oncology center.

424 Background: Oncology pharmacists are uniquely positioned to improve the quality of care provided to patients with cancer within the team-based setting. Some benefits include providing information that supports safe and cost-effective use of medications relative to supportive care management, drug-drug interaction evaluation, patient counseling, answering drug information questions, and reducing physician workload. The primary objective of this study is to evaluate the benefit of a full-time clinical pharmacist in the ambulatory oncology setting through documenting pharmacist-driven clinical interventions, correspondence of those interventions with cost avoidance, and perceived benefit from provider and patient satisfaction surveys. Methods: Pharmacists were in clinic Monday through Thursday, covering three medical oncologists each day. Interventions were documented through EPIC i-Vents. The categories included: adverse events, drug interactions, lab monitoring, medication reconciliation, order clarification, patient education/counseling, drug information, and supportive care. Pharmacist interventions correlated with cost avoidance benefits based on values from prior publications. Weekly surveys were sent to the providers to assess satisfaction of the program and to improve workflow. Results: The data from this pilot program reflects the study period starting January 9, 2023 through June 1, 2023. The financial impact, as calculated from the pharmacist interventions, cost avoidance values and time spent resulted in the estimated cost avoidance of $169,321.10 based on a total of 839 interventions. Other interventions were made that were not given a cost value. Thus, the estimated cost savings is likely an under-estimation of the true cost savings provided from this service during this five-month study period. Extended providers (n = 5) and medical oncologists (n = 3) surveys indicated an overall strong agreement to the benefits of an oncology pharmacist’s involvement in clinic. The pilot program is currently working on a method to gather patient surveys to assess patient satisfaction. Conclusions: This study demonstrates the clinical and financial benefit of an embedded oncology pharmacist in the outpatient oncology clinic. As we move into an era with increasing approvals of oncology medications, coupled with increasing complexity of care, this service may help address some of the unmet gaps to improve collaborative and comprehensive patient-centered care.

Evaluation of drug-drug interactions in critically ill pediatric patients

Purpose: The aim of this study was to determine the drug-drug interactions that are frequently encountered in critically ill patients and the factors that predict these interactions. &#x0D; Materials and Methods: All patients who were admitted to the pediatric intensive care unit (13 bed) of a university hospital and used more than one drug in their treatment were included in this prospective and cross-sectional study. Patients' demographics, laboratory findings, and medications included in their treatment were evaluated daily by a clinical pharmacist. The UpToDate® database was used to detect potential drug interactions. &#x0D; Results: During the study, 797 potential drug-drug interactions were detected in 55 (83.33%) of 66 patients followed. All these interactions were evaluated by the clinical pharmacist and 114 recommendations were made to the physicians following the treatment regarding these potential interactions. Eighty-five (74.56%) of these recommendations were accepted by physicians. Within the scope of the study, each patient was followed up for a median of 9 (2-63) days, and the median value of potential drug interactions detected during this period was calculated as 7 (1-89). &#x0D; Conclusion: The existence of pDDIs was significantly associated with the number of prescribed medications. Exposure to pDDIs is frequent in critically ill pediatric patients and related to the number of medications. Daily and close cooperation between clinicians and clinical pharmacists is recommended to prevent harmful outcomes of DDIs. In order to minimize this risk, it is recommended to avoid polypharmacy as much as possible and to offer alternatives to inducer and inhibitor drugs in treatment.

An Evaluation of Drug-Drug Interaction Alerts Produced by Clinical Decision Support Systems in a Tertiary Hospital.

Introduction Drug-drug interactions (DDIs) have the potential to harm patients. Hence, DDI alerts are meant to prevent harm; as a result, their usefulness is reduced when most alerts displayed to providers are ignored. This study aims to explore the rates and reasons for overriding alerts of DDI. Methods This is a retrospective study of DDI alert overrides that occurred between January 2020 and December 2020 within the inpatient medical records at a tertiary hospital, Medina City, Kingdom of Saudi Arabia. Results A total of 7,098 DDI alerts were generated from inpatient settings, of which 6,551(92.2%) were overridden by the physicians at the point of prescribing. "Will Monitor as Recommended" (33%) was the most common reason for the override, followed by 'Will Adjust the Dose as Recommended (27.1%)," "The Patient Has Already Tolerated the Combination" (25.7%), and "No Overridden Reason Selected" (13.0%). Discussion The DDI alert overriding is still high and is comparable to other studies. However, this study reveals that physicians are ready to deal with the consequences of around 58% of DDI alerts. Additionally, 13% of physicians were not willing to report the reason for overriding. This indicates an urgent need to review and restructure the DDI alert system. Conclusion The DDI alert override rates are high, and this is undesirable. It is recommended to revise the DDI alert system. Future studies should dig deep for real reasons for overriding and seek inputs from all stakeholders, including developing actionable metrics to track and monitor DDI alerting system.

Retrospective Evaluation of Drug-Drug Interactions With Erlotinib and Gefitinib Use in the Military Health System.

Erlotinib and gefitinib are epidermal growth factor receptor-tyrosine kinase inhibitors approved for non-small cell lung cancer treatment by the US Food and Drug Administration. Drug-drug interactions (DDIs) with these agents are vague and poorly understood. Because DDIs can have an effect on clinical outcomes, we aimed to identify drugs that interact with erlotinib or gefitinib and describe their clinical manifestations. A retrospective analysis was performed on the health records of patients in the US Department of Defense Cancer Registry (retrieved September 2021), Comprehensive Ambulatory/Professional Encounter Records, and Pharmacy Data Transaction Service database (both retrieved May 2022). Patients' medical history, diagnoses, and demographics were extracted and analyzed for differences in adverse effects when these agents were used alone vs concomitantly with other prescription drugs. Patients' diagnoses and prescription drug use were extracted to compare completed vs discontinued treatment groups, identify medications commonly co-administered with erlotinib or gefitinib, and evaluate DDIs with antidepressants. Of 387 patients using erlotinib, 264 completed treatments; 28 of 33 patients using gefitinib completed treatment. The P value for erlotinib discontinuation when used alone vs concomitantly was < .001, and the P value for gefitinib discontinuation was .06. Patients who took erlotinib or gefitinib concomitantly with a greater number of prescription drugs had a higher rate of treatment discontinuation than those who received fewer medications. Patients in the completed group received 1 to 75 prescription drugs, and those in the completed group were prescribed 3 to 103. Those who discontinued treatment had more diagnosed medical issues than those who completed treatment. This review cannot conclude that concomitant use with prescription drug(s) resulted in erlotinib or gefitinib discontinuation. There were no significant DDIs determined between erlotinib or gefitinib and antidepressants.