Effects Of Drug Interactions Research Articles

Assessment of Potential Drug-Drug Interactions for Novel Oral Melanocortin-1 Receptor Agonist Dersimelagon.

Dersimelagon is a novel investigational orally administered selective agonist of the melanocortin-1 receptor. The drug-drug interaction (DDI) potential of dersimelagon was investigated in both nonclinical (in vitro) and clinical studies. The invitro inhibition of CYP/UGT isoforms and efflux/uptake transporters by dersimelagon was assessed. The impact of 300-mg dersimelagon on the pharmacokinetics (PK) of substrate drugs and the effect of co-administering verapamil on 100-mg dersimelagon PK (as substrate drug) were investigated in healthy participants in a Phase 1 study. DDIs were assessed based on ratios of Cmax and AUC0-∞ of substrate drug administered alone and with dersimelagon (or verapamil). Relatively potent invitro inhibition of CYP2C9, CYP3A, UGT1A1, BCRP, P-gp, and OATPs by dersimelagon was observed. In the clinical study, exposures of atorvastatin (CYP3A, P-gp, BCRP, OATP substrate) rosuvastatin (BCRP and OATP substrate), and β-hydroxy simvastatin (metabolite of simvastatin) increased 2- to 3-fold (atorvastatin: Cmax LS mean ratio = 198.0%; AUC0-∞ ratio = 196.6%; rosuvastatin: Cmax ratio = 316.5%, AUC0-∞ ratio = 206.0%) when co-administered with dersimelagon. Midazolam (CYP3A substrate), digoxin (P-gp), pravastatin (OATP), and simvastatin (CYP3A) did not show any clinically relevant DDI effects when co-administered with dersimelagon. Dersimelagon exposure increased ~25% when co-administered with verapamil, an effect not considered clinically relevant. Dersimelagon 300 mg did not elicit major DDIs involving CYP/UGT enzymes and drug transporters; however, dersimelagon may have potential for clinically relevant DDIs with drugs that are substrates for BCRP, such as atorvastatin and rosuvastatin, and caution should be exercised when co-administering 300-mg dersimelagon with these statin drugs. Trial Registration: ClinicalTrials.gov: NCT04793295, NCT04402489, NCT04440592, NCT02834442, NCT03520036, NCT03503266.

Effect of drug interactions with apixaban on clinical outcomes in cancer patients with venous thromboembolism

IntroductionIt is unclear how drug-interaction with apixaban influences recurrent venous thromboembolism (VTE) and bleedings in cancer patients.MethodsA post-hoc analysis of a single-arm interventional clinical trial on apixaban treatment of cancer patients with VTE to investigate whether the occurrence of any of the endpoints could be associated with the concurrent use of an interacting drug. Drugs taken by the patients during the trial period were categorized as either increasing bleeding risk, increasing thrombosis risk, both or neither.Results298 patients were divided into groups based on whether they used no interacting drugs (controls, n=74), drugs increasing bleeding risk (n=55), drugs increasing thrombosis risk (n=8), or both (n=161). Odds ratios (OR) were calculated for recurrent VTE, clinically relevant non-major bleeding (CRNMB), and major bleeding during the 36-month follow-up period. Each patient took a median of 13 different drugs over the study period. 67% of the patients used drugs expected to both increase bleeding and thrombosis. The use of fluconazole appeared associated with CRNMB (OR 3.6, 95% confidence interval (CI) 0.99-13), but not with major bleeding (OR 0.56, 95% CI 0.06 - 4.8). Non-steroid anti-inflammatory drugs were not associated with CRNMB (OR 1.0, 95% CI 0.25-4.1) or major bleedings (OR 0.72, 95% CI 0.14 - 3.6). Use of antiplatelet therapy was not associated with CRNMB (OR 0.75, 95% CI, 0.22 - 2.58) or major bleeding (OR 0.2, 95% CI, 0.02-1.6). There were no major bleedings in 23 patients using aprepitant nor in the 10 patients taking macrolides. We found no association between drugs and recurrent VTE, except that there were no recurrent VTE in 19 patients using bevacizumab.ConclusionsDespite the high number of drugs taken that could potentially interact with apixaban, none were found to clearly influence clinical outcomes, except that fluconazole may increase the risk of CRNMB.

Paracetamol, its metabolites, and their transfer between maternal circulation and fetal brain in mono- and combination therapies.

Due to its availability and perceived safety, paracetamol is recommended even during pregnancy and for neonates. It is used frequently alone or in combination with other drugs required for the treatment of various chronic conditions. The aim of this study was to investigate potential effects of drug interactions on paracetamol metabolism and its placental transfer and entry into the developing brain. Sprague Dawley rats at postnatal day P4, pregnant embryonic day E19 dams, and non-pregnant adult females were administered paracetamol (15mg/kg) either as monotherapy or in combination with one of seven other drugs: cimetidine, digoxin, fluvoxamine, lamotrigine, lithium, olanzapine, valproate. Concentrations of parent paracetamol and its metabolites (paracetamol-glucuronide, paracetamol-glutathione, and paracetamol-sulfate) in plasma, cerebrospinal fluid (CSF) and brain were measured by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and their entry into the brain, CSF and transfer across the placenta were estimated. In monotherapy, concentration of parent paracetamol in plasma, CSF, and brain remained similar and at all ages brain entry was unrestricted. In combination therapies, CSF entry of paracetamol increased following co-treatment with olanzapine. Placental transfer of parent paracetamol remained unchanged, however, transfer of paracetamol-sulfate increased with lamotrigine co-administration. Acutely administered paracetamol was more extensively metabolized in adults compared to younger ages resulting in increased concentration of its metabolites with age. Developmental changes in the apparent brain and CSF entry of paracetamol appear to be determined more by its metabolism, rather than by cellular control of its transfer across brain and placental barriers.

Balancing the Interactions: Assessing Antiplatelet and Antiretroviral Therapy Drug-Drug Interactions in People Living With HIV.

The clinical effect of drug-drug interactions (DDIs) between antiplatelets and antiretroviral therapy (ART) on bleeding, thrombosis, and other major adverse cardiovascular events (MACE) is unknown. The objective of this retrospective study was to assess the incidence of DDI at P2Y12 inhibitor (P2Y12inh) initiation and the effect of DDI on patient outcomes. Adult people living with HIV (PLWH) receiving ART newly initiated on an oral P2Y12inh were included. The primary outcome was the incidence of DDI between ART and P2Y12inh at P2Y12inh initiation. Secondary outcomes included bleeding events, MACE, and switches in P2Y12inh. There were 149 PLWH included, of these, 119 (80%) were initiated on clopidogrel, 23 (15%) on ticagrelor, and 7 (5%) on prasugrel. Ninety-three PLWH (60%) had a DDI at time of P2Y12inh initiation, with highest incidence in the clopidogrel group (n = 84, 71%), followed by ticagrelor (n = 9, 39%) and none with prasugrel. Within 1 year, MACE occurred in 12 PLWH, with DDI present at the time of 4 events. There were 29 bleeding events occurring within 1 year, including 17 events with DDI at time of event. However, 88% of DDI in patients with bleeding events were expected to decrease the efficacy of P2Y12inh. Though we observed high incidence of DDI between P2Y12inh and ART in PLWH, MACE and bleeding events at 1 year did not correlate with DDI. It remains unknown whether DDI presence at P2Y12inh initiation with ART causes clinical outcomes of concern, or whether underlying platelet reactivity in PLWH is associated with these events.

Predicting the toxic side effects of drug interactions using chemical structures and protein sequences

The study aims to address the critical issue of toxic side effects resulting from drug combinations, which can significantly increase health risks, clinical complications, and lead to drug being withdrawn from the market. A model named TSEDDI (toxic side effects of drug-drug interaction) has been developed to improve the identification of drug pairs that may induce toxicity or adverse reactions. By utilizing drug chemical structures and diverse proteins, we employ a convolutional neural network (CNN) to extract features from molecular images, enzyme proteins, transporter proteins, and target proteins. Furthermore, we introduce a weighted binary cross entropy loss function to tackle class imbalance and integrate the multi-head attention mechanism with residual connections to enhance model performance. Our model outperformed advanced baseline models in predicting drug-drug interaction (DDI) side effects, achieving an accuracy of 0.9059 (± 0.0010) and consistently excelling across various evaluation metrics. The case study confirms the potential mechanisms by which four pairs of drugs cause side effects, thus demonstrating the effectiveness of our model in predicting DDI side effects. The TSEDDI model combines multiple attention mechanisms and residual connections, enhancing its ability to detect toxic and adverse effects related to DDIs. As a result, it becomes a valuable resource for promptly identifying adverse reactions in clinical trials. Future research could investigate drug substructures prone to toxic side effects.

Safety of Nirmatrelvir-Ritonavir Administration in Children With Immunodeficiency and/or Comorbidities With SARS-CoV-2 Infection: A Retrospective Clinical Report.

Despite the generally mild course of COVID-19 in children, immunocompromised patients may experience complications or severe infection. This study reports the clinical outcomes of pediatric patients treated with nirmatrelvir and ritonavir (N/R) for SARS-CoV-2 infection. We retrospectively reported the data of children with any immunodeficiency with COVID-19 who received N/R treatment between March 2022 and June 2023 at the Bambino Gesù Children's Hospital. Patients were treated with N/R for 5 days. We compared liver and kidney function before and after treatment with N/R and looked for a relationship between the duration of COVID-19 infection and the time from positivity to administration of N/R administration. A total of 85 pediatric immunocompromised patients with COVID-19 were included in the study, with a mean age of 10.7 years (SD 4.8), mostly males (60%). We found a significant difference in the viral load before and after N/R administration. Four patients (4.7%) experienced adverse events related to N/R therapy. One of these had to discontinue N/R administration. Three patients (3.5%) experienced negative effects of drug interactions during N/R therapy, namely an increase of sirolimus and ciclosporin serum levels. A significant positive correlation was found between the time from SARS-CoV-2 positivity to N/R administration and the duration of SARS-CoV-2 swab positivity (R = 0.78, P < 0.001), suggesting that the earlier N/R is administered, the shorter the duration of COVID-19 in the study sample. Our experience shows that N/R is reasonably safe in the pediatric population and could favor viral clearance, thus reducing the duration of infection.

Severe Impact of Omeprazole Timing on pH-Sensitive Dasatinib Absorption: Unveiling Substantial Drug-Drug Interaction.

The absorption and bioavailability of most tyrosine kinase inhibitors are affected by gastrointestinal pH as they are weak basic lipophilic drugs. Hence, concomitant use of acid reducing agents (ARAs) is frequently restricted. Particularly comedication of crystalline dasatinib (Sprycel) and proton-pump inhibitors (PPIs) should be avoided. Drug-drug interaction (DDI) studies with PPIs report approximately 40%-80% bioavailability reduction of dasatinib. Limitations in the design of these studies do not allow for assessing the near maximum DDI as timing of PPI dosing was either not reported or 22 h prior to dasatinib intake. We conducted a DDI study of crystalline dasatinib and omeprazole in healthy, fasted participants, investigating the impact of PPI comedication on dasatinib plasma exposure at a time point when the near maximum DDI effect is expected. Participants were administered omeprazole (day 2-5) to reach steady state. On day 6, a single dose of crystalline dasatinib was given. Crystalline dasatinib dosing alone on day 1 served as control (single dose). The dosing interval between omeprazole administration and crystalline dasatinib was 10 h (median [range: 9-10 h]). Dasatinib Cmax and AUC0-24 were reduced by 96% and 89% by omeprazole comedication. Cmax was 224.6 ± 104.7 ng/mL (mean ± SD) and 8.0 ± 4.5 ng/mL (P <.0001) and AUC0-24 was 797.6 ± 274.5 and 90.6 ± 38.1 h·ng/mL (P <.0001) without and with omeprazole. T1/2 was 5.7 ± 1.5 h (mean ± SD) with crystalline dasatinib dosing alone and could not be reliably calculated with comedication. To ensure optimal patient outcome, it is vital to investigate bioavailability of pH-sensitive drugs at the maximal DDI effect of ARAs to understand the worst-case influence for efficient clinical management.

Physiologically based pharmacokinetic modeling of drug-drug interactions between ritonavir-boosted atazanavir and rifampicin in pregnancy.

Ritonavir-boosted atazanavir (ATV/r) and rifampicin are mainstays of second-line antiretroviral and multiple anti-TB regimens, respectively. Rifampicin induces CYP3A4, a major enzyme involved in atazanavir metabolism, causing a drug-drug interaction (DDI) which might be exaggerated in pregnancy. Having demonstrated that increasing the dose of ATV/r from once daily (OD) to twice daily (BD) in non-pregnant adults can safely overcome this DDI, we developed a pregnancy physiologically based pharmacokinetic (PBPK) model to explore the impact of pregnancy. Predicted pharmacokinetic parameters were validated with separate clinical datasets of ATV/r alone (NCT03923231) and rifampicin alone in pregnant women. The pregnancy model was considered validated when the absolute average fold error (AAFE) for Ctrough and AUC0-24 of both drugs were <2 when comparing predicted vs. observed data. Thereafter, predicted atazanavir Ctrough was compared against its protein-adjusted IC90 (14 ng/mL) when simulating the co-administration of ATV/r 300/100 mg OD and rifampicin 600 mg OD. Pregnancy was predicted to increase the rifampicin DDI effect on atazanavir. For the dosing regimens of ATV/r 300/100 mg OD, ATV/r 300/200 mg OD, and ATV/r 300/100 mg BD (all with rifampicin 600 mg OD), predicted atazanavir Ctrough was above 14 ng/mL in 29%, 71%, and 100%; and 32%, 73% and 100% of the population in second and third trimesters, respectively. Thus, PBPK modeling suggests ATV/r 300/100 mg BD could maintain antiviral efficacy when co-administered with rifampicin 600 mg OD in pregnancy. Clinical studies are warranted to confirm safety and efficacy in pregnancy.

The Impact of QT-Prolonging Medications and Drug-Drug Interactions on QTc Interval Prolongation in Hospitalized Patients: A Case-Crossover Study.

Researchers have studied potential corrected QT interval (QTc) prolongation from drug-drug interactions (DDIs), raising unresolved questions about their real-world impact. This retrospective case-crossover study investigated the effects of QT-prolonging drugs and DDIs on QTc prolongation in hospitalized patients aged 45 years and above. The cohort comprised patients who had multiple hospitalizations and developed QTc prolongation (QTc > 500 ms or an increase of >60 ms from baseline) at least 24 hours after admission between 2011 and 2019. Conditional logistic regression compared drug exposure between hospitalizations with QTc prolongation (case window) and those without (reference window). Among 2,276 patients (mean age 71; 43.8% female), the use of QT-prolonging drugs significantly increased the risk of QTc prolongation (odds ratio: 2.42 (95% confidence interval: 1.95-3.02)). The risk was higher with drugs of "known risks" (OR: 3.78 (2.91-4.90)) and "conditional risk" (OR: 2.08 (1.65-2.62)). DDIs, particularly involving multiple "known risk" drugs (OR: 7.86 (4.96-12.45)), strong cytochrome P450 enzyme inhibitors (OR: 5.57 (2.75-11.30)), or the concurrent use of ≥4 QT-prolonging drugs with any risk (OR: 5.28 (3.96-7.03)) substantially increased the risk. Cautious prescribing for patients with multiple risk factors is important to minimize the likelihood of QTc prolongation. However, when considering enhanced monitoring or drug choices, it is crucial to carefully evaluate the overall risk of QT prolongation against the benefits of treatment to ensure optimal patient care.

Association between Drug Use and Perception of Mental Health in Women Diagnosed with Fibromyalgia: An Observational Study.

Fibromyalgia (FM) is a chronic syndrome characterized by widespread musculoskeletal pain, fatigue, sleep disturbances, and mental health issues. It affects approximately 1.78% of the general population; an estimated 4:1 ratio between women and men is observed. It significantly impacts quality of life and carries both clinical and social stigma. This study aims to evaluate the relationship between drug use and mental health in female patients with fibromyalgia. This study is prospective, observational, and cross-sectional. A questionnaire was administered to 544 subjects, achieving a representative sample size from a population of 800,000 subjects by using an algorithm for proportion estimation with a known sampling frame. The selection was non-random, making the sampling non-probabilistic. Logistic regression models were applied to assess the effect of drug use on perception of mental health; presence of symptoms such as comprehension and memory problems, insomnia, depression, and anxiety; and severity of cognitive symptoms and non-restorative sleep. To quantify the impact, odds ratios and confidence intervals have been observed. The findings indicate the non-recommended use of medications and reveal the ineffectiveness and adverse effects of drug interactions on mental health. The use of benzodiazepines and sedative-hypnotics is significantly associated with a negative perception of mental health. Benzodiazepines do not improve symptoms or significantly reduce their severity. SSRI antidepressants do not enhance mental health perception; however, when used exclusively, they are effective in reducing the severity, but not the prevalence, of cognitive symptoms. The results highlight the complexity of pharmacological management in FM and raise concerns about the inappropriate use of ineffective or counterproductive drug interactions affecting patients' mental health. They underscore the need for multidisciplinary and personalized strategies that include close and careful monitoring, as well as the simultaneous use of non-pharmacological treatments that have demonstrated evidence in improving quality of life without negatively affecting mental health, such as patient education, psychological therapy, physiotherapy, and mindfulness.

Physiologically based pharmacokinetic modelling to predict potential drug-drug interactions of dersimelagon (MT-7117).

Dersimelagon is a novel, investigational, orally administered, selective agonist of the melanocortin-1 receptor that has demonstrated efficacy at increasing symptom-free light exposure and an acceptable safety profile in patients with protoporphyria. A phase 1 drug-drug interaction (DDI) study demonstrated that dersimelagon 300 mg has the potential for clinically relevant DDIs with drugs that are substrates for breast cancer resistance protein, such as atorvastatin and rosuvastatin. This study uses physiologically based pharmacokinetic (PBPK) modelling to further investigate the DDI effects at lower doses of dersimelagon with substrate drugs. The data from in silico, in vitro and in vivo studies were used to construct a PBPK model for dersimelagon to assess the DDI potential between dersimelagon and substrate drugs for cytochrome P450 3A, P-glycoprotein, organic anion transporting polypeptide 1B1/1B3, organic anion transporter 3 and breast cancer resistance protein, including atorvastatin and rosuvastatin. The systemic exposure of atorvastatin based on the maximum plasma concentration and area under the plasma concentration-time curve was predicted to increase 1.21-fold and 1.25-fold, respectively, if coadministered with dersimelagon 100 mg, and 1.42-fold and 1.45-fold with dersimelagon 200 mg. The systemic exposure of rosuvastatin followed trends similar to atorvastatin (1.67-fold and 1.34-fold increase in maximum plasma concentration and area under the plasma concentration-time curve, respectively, with dersimelagon 100 mg, and 2.40-fold and 1.69-fold with dersimelagon 200 mg). Overall, PBPK modelling results indicate that the simulated changes in plasma exposure of atorvastatin and rosuvastatin following coadministration with dersimelagon 100 or 200 mg are not clinically significant, but caution and appropriate clinical monitoring should be recommended.

The Prevalent New-User Design to Study Drug-Drug Interactions: The Example of Sulfonylureas and Warfarin Interaction on the Risk of Severe Hypoglycemia.

The optimal design for pharmacoepidemiologic drug-drug interactions (DDIs) studies is unclear. Using the association between concomitant use of sulfonylureas and warfarin and the risk of severe hypoglycemia as a case study, a DDI with little or no clinical impact, we tested whether the prevalent new-user design can be applied in the area. Among all patients initiating sulfonylureas in the UK's Clinical Practice Research Datalink (1998-2020), we identified those adding-on warfarin while on a sulfonylurea. For each co-exposed patient, we defined a prescription-based exposure set including other sulfonylurea users not adding-on warfarin (comparators). Within each exposure set, we matched each co-exposed patient to five comparators on time-conditional propensity scores (TCPS) and followed them using an as-treated approach. Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) of severe hypoglycemia associated with concomitant use of sulfonylureas and warfarin compared to use of sulfonylureas alone. Sensitivity analyses addressed the impact of different potential sources of bias. The study cohort included 17 890 patients co-exposed to sulfonylureas and warfarin and 88 749 matched comparators. After TCPS matching, patient characteristics were well-balanced between groups. Compared to use of sulfonylureas alone, concomitant use of sulfonylureas and warfarin was not associated with the risk of severe hypoglycemia (HR, 1.04; 95% CI, 0.92-1.17). Sensitivity analyses were consistent with the primary analysis (HRs ranging from 1.01 to 1.15, all not statistically significant). Our study suggests that the prevalent new-user design could be used for the assessment of clinical effects of DDIs.

Predict the Drug-Drug Interaction of a Novel PI3Kα/δ Inhibitor, TQ-B3525, and Its Two Metabolites Using Physiologically Based Pharmacokinetic Modeling.

A novel dual PI3K α/δ inhibitor, TQ-B3525, has been developed for the targeted treatment of lymphoma and solid tumors. TQ-B3525 is primarily metabolized by CYP3A4 and FOM3, while also serving as a substrate for the P-glycoprotein transporter. The aim of this study was to anticipate the drug-drug interaction (DDI) of TQ-B3525 and its two metabolites with CYP3A4 enzyme potent inducer (rifampicin) and CYP3A4/P-gp inhibitor (itraconazole) utilizing a physiologically based pharmacokinetic (PBPK) modeling approach. Clinical data from healthy and cancer patient adults were employed to construct and evaluate the PBPK model for TQ-B3525, M3, and M8-3. Models involving rifampicin combined with midazolam, itraconazole combined with midazolam or digoxin were utilized to showcase the robustness of evaluating DDI effects. The simulated drug exposure of TQ-B3525, M3, and M8-3 in healthy and patient adults were consistent with clinical data, and the mean fold error values were within the acceptable ranges. The simulated results of positive substrates correspond to those reported in the literature. Co-administration with rifampicin reduces Cmax and AUC of TQ-B3525 to 76.1% and 46.0%, while increasing the levels of M3 and M8-3. With itraconazole, Cmax and AUC of TQ-B3525 rise to 131% and 204%, but decrease substantially for M3 and M8-3. PBPK model simulation results showed that the systemic exposure of TQ-B3525 was significantly affected when co-administered with CYP3A4/P-gp inducers and inhibitors. This indicates that the combination with strong inducers and inhibitors should be carefully avoided or adjust the dosage of TQ-B3525 in clinic.

Physiologically-based pharmacokinetic modeling predicts the drug interaction potential of GLS4 in co-administered with ritonavir.

GLS4 is a first-in-class hepatitis B virus (HBV) capsid assembly modulator (class I) that is co-administered with ritonavir to maintain the anticipated concentration required for the effective antiviral activity of GLS4. In this study, the first physiologically-based pharmacokinetic (PBPK) model for GLS4/ritonavir was successfully developed. The predictive performance of the PBPK model was verified using data from 39 clinical studies, including single-dose, multiple-dose, food effects, and drug-drug interactions (DDI). The PBPK model accurately described the PK profiles of GLS4 and ritonavir, with predicted values closely aligning with observed data. Based on the verified GLS4/ritonavir model, it prospectively predicts the effect of hepatic impairment (HI) and DDI on its pharmacokinetics (PK). Notably, CYP3A4 inducers significantly influenced GLS4 exposure when co-administered with ritonavir; co-administered GLS4 and ritonavir significantly influenced the exposure of CYP3A4 substrates. Additionally, with the severity of HI increased, there was a corresponding increase in the exposure to GLS4 when co-administered with ritonavir. The GLS4/ritonavir PBPK model can potentially be used as an alternative to clinical studies or guide the design of clinical trial protocols.

Kajian efek interaksi antihipertensi dengan obat antiinflamasi nonsteroid (OAINS) terhadap tekanan darah pada pasien hipertensi

Hypertension causes atherosclerosis which can cause occlusion resulting in subchondral ischemia, which causes the exchange of nutrients and gases into the articular cartilage, which is disrupted and becomes the potential initiator of degradative changes or the so-called osteoarthritis. This review aims to evaluate the effect of antihypertensive drug interactions with NSAIDs on blood pressure. This article review was conducted using a narrative review study. The inclusion criteria in this article are the pharmacodynamic interaction of antihypertensives with NSAIDs in hypertensive patients with osteoarthritis. The results obtained seven articles that meet the inclusion criteria. Hypertensive patients with osteoarthritis receive medications that must be monitored because a drug-drug interaction can decrease the effectiveness of antihypertensives. One of the most widely used drugs in antihypertensive therapy is ACEi, but NSAIDs inhibit the antihypertensive effect on prostaglandin synthesis resulting from bradykinin production. The decrease in blood pressure stimulated by bradykinin plays an essential role in vasodilatation. The interaction of ACEi and NSAIDs can cause an increase in blood pressure with a minimum expansion of 1.1 mmHg. Still, when administering NSAIDs with CCBs, a decrease in blood pressure of 2.86 mmHg is obtained because the action is not dependent on prostaglandins. Based on the results, there were pharmacodynamic changes for both antihypertensive drugs and NSAIDs due to drug interactions that affected the patient's clinical outcome in the form of blood pressure.

Abstract 4310: Impact of drug combination schedules on the evolution of tumor resistance

Abstract The emergence of drug resistance during treatment is a critical limiting factor for the long-term effectiveness of anticancer therapies. The use of drug combinations limits the emergence of resistance, thereby prolonging their therapeutic utility. The effect of a drug combination is dependent both on the concentration of the individual drugs and the extent of their interaction (synergistic, additive, or antagonistic). Thus, understanding the scheduling implications for efficacy can be valuable for optimizing tumor response and time to resistance for the combination. To examine this more closely, we used a pharmacokinetic/pharmacodynamic (PK/PD) modeling approach, coupling one-compartment PK models of two hypothetical drugs based on set synchronous or asynchronous (offset) schedules with an evolutionary model of tumor clone growth. We simulated four tumor subpopulations representing binary resistance to one or both drugs, using an exponential growth model with growth penalties based on drug concentrations. We used the model to simulate the effects of pairs of antagonistic, synergistic, and additive drugs on the growth of sensitive and resistant populations in a tumor. Final tumor volume and resistant population were used to compare the efficacy of various drug schedules. As expected, the effect of a drug interaction—either synergistic or antagonistic—is greatest when dosing is synchronous, as this results in the greatest simultaneous drug concentration. The effect of a drug interaction is proportional to the sensitive population size, so the effect of drug interactions decreases over time as the sensitive population is eliminated. Intuitively, the final tumor volume decreases as the strength of a synergistic interaction increases until the sensitive population is eliminated. The resistant populations are unaffected by the strength of the interaction for both antagonistic and synergistic combinations. Resistant populations are not affected by interactions between drugs to which they are resistant, so the growth of resistant populations over time is independent of drug interactions. Crucially, for effective drugs—that is, drugs that eliminate the sensitive population—synergy and antagonism converge to additivity as the sensitive population dies out because the remaining resistant populations are not affected by drug interactions. As synergistic and additive drug combinations are equivalent in the long-term, synergy and asynchronous dosing could be used to limit toxicity without sacrificing efficacy. The evolution of resistance also explains why in vitro synergy or antagonism may not translate in vivo. Taken together, our work provides several unintuitive insights about combination drug scheduling with practical implications for therapeutic design. Citation Format: Madison Stoddard, Andrew Chen, Lin Yuan, Debra Van Egeren, Dean Bottino, Arijit Chakravarty. Impact of drug combination schedules on the evolution of tumor resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4310.

Abstract 4761: Understanding three-drug combinations: optimizing scheduling while avoiding toxicity

Abstract The promise of combination therapies in managing tumor resistance is constrained by the therapeutic window (the difference between efficacious and toxic doses). We have sought to understand the impact of dose scheduling on the emergence of resistance, using a mathematical model-based approach linking pharmacokinetics (PK) to a parsimonious evolutionary model of tumor growth incorporating the emergence of drug resistance. Here, we analyze the potential of a three-drug combination to minimize toxicity while preventing the emergence of resistance. We simulated eight tumor subpopulations representing resistance to one, two or three hypothetical drugs, using an exponential growth model with growth penalties based on drug concentrations. We used the model to simulate the effects of pairs of antagonistic, synergistic, and additive drugs on the growth of sensitive and resistant populations in a tumor. Final tumor volume and resistant population were used to compare the efficacy of various drug schedules. We assumed an overlapping neutropenia-like toxicity as the dose-limiting toxicity for the three drug combination, which we have previously shown to be proportional to the peak moving average drug concentration over 18 days. Final tumor volume and peak toxicity were used to compare the effectiveness of various dosing strategies and interaction combinations. Synchronous dosing results in the greatest drug interaction effect between any two drugs. When all interactions between the three drugs are either antagonistic or synergistic, the greatest effect is observed when all drugs are dosed simultaneously. The drugs are most toxic when all three are dosed simultaneously or nearly simultaneously. Our work shows that time sensitivity—that is, the magnitude of the changes in efficacy associated with small changes in schedule—varies with phase offset. Scheduling is most time sensitive when all drugs are dosed simultaneously or nearly simultaneously; dosing of two drugs simultaneously is less time sensitive. Three-drug combinations are an effective means of preventing complete resistance. While simultaneous dosing of two or more drugs confers the greatest efficacy, such simultaneous dosing results in greater peak toxicity because of higher simultaneous drug concentrations. A potential compromise is the simultaneous dosing of two of the three drugs, which results in an intermediate toxicity. Dosing of two drugs simultaneously is also less time sensitive, advantageous in a practical sense as it is more robust to patient dosing error. While the approach used here is based on hypothetical drug properties, it provides a framework that is readily applicable to real-world drug combinations, with the potential for practical insights to guide the framing of the Target Product Profile for three-drug combinations. Citation Format: Madison Stoddard, Lin Yuan, Debra Van Egeren, Andrew Chen, Dean Bottino, Arijit Chakravarty. Understanding three-drug combinations: optimizing scheduling while avoiding toxicity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4761.

Two-way pharmacodynamic modeling of drug combinations and its application to pairs of repurposed Ebola and SARS-CoV-2 agents.

Existing pharmacodynamic (PD) mathematical models for drug combinations discriminate antagonistic, additive, multiplicative, and synergistic effects, but fail to consider how concentration-dependent drug interaction effects may vary across an entire dose-response matrix. We developed a two-way pharmacodynamic (TWPD) model to capture the PD of two-drug combinations. TWPD captures interactions between upstream and downstream drugs that act on different stages of viral replication, by quantifying upstream drug efficacy and concentration-dependent effects on downstream drug pharmacodynamic parameters. We applied TWPD to previously published in vitro drug matrixes for repurposed potential anti-Ebola and anti-SARS-CoV-2 drug pairs. Depending on the drug pairing, the model recapitulated combined efficacies as or more accurately than existing models and can be used to infer efficacy at untested drug concentrations. TWPD fits the data slightly better in one direction for all drug pairs, meaning that we can tentatively infer the upstream drug. Based on its high accuracy, TWPD could be used in concert with PK models to estimate the therapeutic effects of drug pairs in vivo.

AutoDDI: Drug-Drug Interaction Prediction With Automated Graph Neural Network.

Drug-drug interaction (DDI) has attracted widespread attention because when incompatible drugs are taken together, DDI will lead to adverse effects on the body, such as drug poisoning or reduced drug efficacy. The adverse effects of DDI are closely determined by the molecular structures of the drugs involved. To represent drug data effectively, researchers usually treat the molecular structure of drugs as a molecule graph. Then, previous studies can use the handcrafted graph neural network (GNN) model to learn the molecular graph representations of drugs for DDI prediction. However, in the field of bioinformatics, manually designing GNN architectures for specific molecular structure datasets is time-consuming and depends on expert experience. To address this problem, we propose an automatic drug-drug interaction prediction method named AutoDDI that can efficiently and automatically design the GNN architecture for drug-drug interaction prediction without manual intervention. To this end, we first design an effective search space for drug-drug interaction prediction by revisiting various handcrafted GNN architectures. Then, to efficiently and automatically design the optimal GNN architecture for each drug dataset from the search space, a reinforcement learning search algorithm is adopted. The experiment results show that AutoDDI can achieve the best performance on two real-world datasets. Moreover, the visual interpretation results of the case study show that AutoDDI can effectively capture drug substructure for drug-drug interaction prediction.

Effect of drug interactions with non-vitamin-K oral anticoagulants on thromboembolic events in patients with nonvalvular atrial fibrillation.

Few real-world studies have investigated drug-drug interactions (DDIs) involving non-vitamin-K antagonist oral anticoagulants (NOACs) in patients with nonvalvular atrial fibrillation (NVAF). The interactions encompass drugs inducing or inhibiting cytochrome P450 3A4 and permeability glycoprotein. These agents potentially modulate the breakdown and elimination of NOACs. This study investigated the impact of DDIs on thromboembolism in this clinical scenario. Patients who had NVAF and were treated with NOACs were selected as the study cohort from the National Health Insurance Research Database of Taiwan. Cases were defined as patients hospitalised for a thromboembolic event and who underwent a relevant imaging study within 7 days before hospitalisa-tion or during hospitalisation. Each case was matched with up to 4 controls by using the incidence density sampling method. The concurrent use of a cytochrome P450 3A4/permeability glycoprotein inducer or inhibitor or both with NOACs was identified. The effects of these interactions on the risk of thromboembolic events were examined with univariate and multivariate conditional logistic regressions. The study cohort comprised 60,726 eligible patients. Among them, 1288 patients with a thromboembolic event and 5144 matched control patients were selected for analysis. The concurrent use of a cytochrome P450 3A4/permeability glycoprotein inducer resulted in a higher risk of thromboembolic events (adjusted odds ratio [AOR] 1.23, 95% confidence interval [CI] 1.004-1.51). For patients with NVAF receiving NOACs, the concurrent use of cytochrome P450 3A4/ permeability glycoprotein inducers increases the risk of thromboembolic events.