Clinical Pharmacology & Therapeutics最新文献

The benefits of sodium-glucose transport protein 2 inhibitor (SGLT2i) use on severe urolithiasis requiring surgery remains unclear. All patients with incident T2D in Taiwan National Health Institution databases (2016-2021) and TriNetX datasets (2014-2023) were retrospectively analyzed. The study analyzed a propensity score-matched pairs with T2D treated with SGLT2i or dipeptidyl peptidase 4 inhibitors (DPP4i). The primary outcome was the incidence of urolithiasis and urolithiasis requiring surgery during the study period. Urolithiasis diagnoses were identified using International Classification of Diseases diagnostic codes and categorized into upper and lower urinary tract stones. Cases of urolithiasis requiring surgery were determined by the presence of both diagnostic codes and surgical procedure codes within the same outpatient visit or hospitalization. Conditional and time-dependent Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). During the study period, 5700 participants were diagnosed with urolithiasis, 1297 participants were urolithiasis requiring surgery in Taiwan NHIRD cohort 8438 participants with urolithiasis as well as 289 participants with urolithiasis requiring surgery were in the TriNetX cohort. Adjusted HRs of urolithiasis and urolithiasis requiring surgery were 0.82-fold (95% CI, 0.77-0.87), 0.72-fold (95% CI, 0.63-0.82) in Taiwan NHIRD, 0.84 (95% CI, 0.78-0.90), and 0.62 (95% CI, 0.44-0.88) in TriNetX cohort respectively. Similar protective associations with SGLT2i use against urolithiasis were observed across subgroups in both datasets from Taiwan NHIRD and TriNetX. In conclusion, SGLT2i might protect against kidney stones and severe cases requiring surgery in T2D patients.

Statin pharmacogenetic implementation guidelines are derived from evidence of primarily Eurocentrically biased study populations. Functional SLCO1B1 variants that are rare in these study populations have not been equitably investigated and are thus missing from guidelines. The objective of this precision medicine health equity study was to determine the clinical validity of understudied candidate functional SLCO1B1 variants common in people with 1,000 Genomes sub-Saharan African superpopulation (1KG-AFR-like) genetic similarity. We conducted our analyses using the real-world evidence of participants from three large, electronic health record-linked biobanks. We used bilirubin levels (as an endogenous substrate of organic anion transporting polypeptide [OATP1B1] function) and severe statin-induced myotoxicity phenotypes. Loss-of-function splice variant rs77271279 (P = 1.1 × 10^-17) had the strongest association with elevated total bilirubin levels in Black participants (mean 84% AFR-like genetic similarity) followed by missense variant rs59502379 (P = 7.4 × 10^-12) then missense variant rs4149056 (P = 6.0 × 10^-5). In an exploratory subset of the Black study population who used statins (n = 77 severe statin-induced myotoxicity cases), rs59502379 (odds ratio [OR] = 2.85, 95% confidence interval [CI] 1.08-7.52), but not rs77271279 (OR = 1.75, 95% CI 0.62-4.73) was associated with myotoxicity. Sensitivity analyses in participants with >5% AFR-like genetic similarity corroborated these findings. For white participants, rs77271279 and rs59502379 were rare precluding subsequent analyses. Our findings highlight the clinical relevance for understudied SLCO1B1 variants on pharmacogenetic testing panels with a potential immediate impact on reducing the risk of severe statin-induced myotoxicity primarily in Black patients, a group historically excluded from genomic research. Future studies require larger statin user study populations with less heterogeneity by statin type.

Characterizing heterogeneity of treatment effects (HTE) is a fundamental goal of pharmacoepidemiology, addressing why medications work differently across patient populations. This paper reviews state-of-the-art methods for studying HTE using real-world data (RWD), which offer larger study sizes and more diverse patient populations compared to randomized clinical trials. The paper first defines HTE and discusses its measurement. It then examines three leading approaches to studying HTE: subgroup analysis, disease risk score (DRS) methods, and effect modeling methods. Subgroup analyses offer simplicity, transparency, and provide insights into drug mechanisms. However, they face difficulties in resolving which subgroup or combination of characteristics should be the basis for clinical decision making when multiple effect modifiers are present. DRS methods address some of these limitations by incorporating multiple patient characteristics into a summary score of outcome risk but may obscure insights into mechanisms. Effect modeling methods directly predict individual treatment effects, offering potential for precise HTE characterization, but are prone to model misspecification and may not provide mechanistic insights. The methods each have tradeoffs. Subgroup analysis is straightforward but can lead to spurious associations and does not account for multiple characteristics at once. DRS methods are relatively simple to implement and clinically useful, but may not completely describe HTE or provide mechanistic insight. Effect modeling approaches have great potential for characterizing HTE but are still being developed. Understanding HTE is essential for personalizing treatment strategies to improve patient outcomes. Researchers must weigh the strengths and limitations of each approach when using RWD to study HTE.

Non-randomized studies will remain the mainstay for evidence on medications' effects in pregnancy since the number of pregnant participants in randomized clinical trials is insufficient to evaluate uncommon but serious pregnancy outcomes. There has been a growing interest in conceptualizing causal inference based on observational data as an attempt to emulate a hypothetical randomized trial: the target trial. This approach can help identify design flaws and ensuing biases and can point toward potential solutions. Adoption of the target trial emulation framework in perinatal studies raises unique challenges due to the distinct role of gestational time. Challenges include, among others, identifying the timing of conception, pregnancy losses as competing events for later outcomes, different etiologically relevant time windows depending on the outcome, and time-varying outcome risks. We discuss various considerations in developing a protocol for a target trial evaluating drug effects in pregnancy and its observational emulation in databases and registries. While not a panacea, the framework offers a valuable tool to guide us through the specification of the causal questions, the study population and the treatment strategies to be compared and helps to identify avoidable biases as well as unavoidable deviations from the optimal protocol. Making these deviations explicit elucidates the assumptions we make when drawing causal conclusions, and the types of analyses that can be undertaken to quantify the potential magnitude of such biases. Such discipline in the design, conduct, and reporting of pregnancy studies will ultimately lead to the best information possible to inform treatment decisions during pregnancy.

A clinical trial was carried out to investigate the pharmacogenetics of single-dose pravastatin and pitavastatin pharmacokinetics in 173 and 164 healthy participants. Additionally, 96 participants were included from previous pharmacogenetic studies with pravastatin. In a genome-wide meta-analysis of pravastatin including all 269 participants, SLCO1B1 c.521T>C (rs4149056) was associated with increased AUC_0-∞ (P = 9.8 × 10^-12). Similarly, SLCO1B1 c.521T>C was genome-wide significantly associated with increased AUC_0-∞ of pitavastatin (P = 9.7 × 10^-15). Candidate gene analyses suggested that participants with increased function SLCO1B1 variants had decreased pravastatin exposure. Furthermore, decreased function CYP2C9 variants may increase pitavastatin and pitavastatin lactone exposure. Compared to participants with normal function SLCO1B1 genotype, the AUC_0-∞ of pravastatin was 140% (90% confidence interval: 86-210%; P = 4.7 × 10^-8) and 37% (20-56%; P = 1.1 × 10^-4) greater in participants with poor and decreased function SLCO1B1 genotype, respectively, while participants with highly increased function SLCO1B1 genotype had a 60% (39-75%; P = 6.0 × 10^-4) lower AUC_0-∞. The AUC_0-∞ of pitavastatin was 153% (100-222%; P = 1.6 × 10^-9) and 35% (8-69%; P = 0.027) greater in participants with poor and decreased function SLCO1B1 genotype, respectively, than in those with normal function SLCO1B1 genotype. Participants with intermediate metabolizer CYP2C9 genotype had 18% (3-34%; P = 0.046) greater AUC_0-∞ of pitavastatin than those with normal metabolizer CYP2C9 genotype. This study demonstrates the important role of SLCO1B1 in pravastatin and pitavastatin pharmacokinetics and suggests that CYP2C9 variants also affect the pharmacokinetics of pitavastatin.

Tailoring pharmacogenomic (PGx) implementation to diverse populations is vital to promoting health equity. We assessed prescriptions for medications with potentially actionable PGx information to identify patient characteristics associated with differential PGx medication exposure. We analyzed the nationally-representative MEPS dataset of adults who reported receiving prescriptions between 2014 and 2021. PGx medications include those the FDA and CPIC designate as having drug-gene associations supported by scientific evidence. With the primary outcome being PGx prescriptions, we performed Poisson regression adjusted for all other relevant covariates. In our final population (N = 119,722, 72% White/20% Black/4% Asian/8% Hispanic), 61% were prescribed a PGx medication, 56% were female, and 97% held health insurance coverage. Adults with private health insurance (65%) or public Medicaid/Medicare coverage (32%) were more likely to have PGx prescriptions than the uninsured (3%). Individuals with cardiovascular conditions [adjusted IRR (aIRR) = 1.45, 95% CI 1.41, 1.48], high cholesterol [aIRR = 1.37, 95% CI 1.35, 1.40], high blood pressure [aIRR = 1.14, 95% CI 1.12, 1.16], and cancer [aIRR = 1.02, 95% CI 1.00, 1.04] were more likely to receive PGx prescriptions. Self-reported Blacks were less likely than Whites to receive PGx medications [aIRR = 0.92, 95% CI 0.90, 0.94], and among those with health conditions, the likelihood of PGx medication exposure for underrepresented groups (Blacks, Hispanics, and Asians) was lower than for Whites. Our study using a comprehensive list of PGx medications in a nationally representative dataset indicates that certain populations are differentially exposed to genomically informed medications. This may suggest that if implementing a pharmacogenomics program based on reactive testing initiated by a prescription, a small underrepresentation of the Black population could be expected because of an underlying prescription disparity. Secondly, if implementing a pharmacogenomics program based on targeted preemptive testing, using clinical indication/comorbidity may be a reasonable way to enrich the population for prescriptions that would benefit from genotype tailoring.

Psychedelics, such as psilocybin and lysergic acid diethylamide (LSD), are being investigated for the treatment of depressive and anxiety disorders, for which concomitant treatment with selective serotonin reuptake inhibitors (SSRIs) is prevalent. The present study investigated the acute response to single doses of LSD (100 μg) after daily administration of paroxetine (10 mg for 7 days, followed by 20 mg for 35 days) or placebo (42 days) using a randomized, double-blind, cross-over design in 23 healthy participants. Paroxetine did not alter pleasant subjective effects of LSD but significantly reduced "bad drug effect," "anxiety," and "nausea." No differences in autonomic effects or QTc interval after LSD administration were found between both conditions. The strong cytochrome P450 2D6 (CYP2D6) inhibitor paroxetine led to higher maximal concentrations and total exposures of LSD (geometric mean ratios of 1.4 and 1.5, respectively) indicating relevant involvement of CYP2D6 in its metabolism. The extent of this inhibition was nominally highest in genetic CYP2D6 normal metabolizers and lowest in poor metabolizers. The present findings suggest that add-on treatment with LSD to an SSRI is well-tolerated. The pharmacokinetic and pharmacodynamic interactions indicate that no dose adjustment of LSD seems necessary in the presence of an SSRI that inhibits CYP2D6. For SSRIs that do not relevantly inhibit CYP2D6, a dose increase of LSD might be appropriate, but due to lacking data and potential other pharmacokinetic interactions with these compounds, no definitive dose recommendation can be made.

Surrogate endpoints, such as progression-free survival (PFS) and objective response rate (ORR), are increasingly used in oncology trials to expedite drug development and decision making. This paper evaluates the effectiveness of these surrogate endpoints by analyzing their correlations with overall survival (OS) across different cancer types, treatments, and therapy lines at both the patient and trial levels using an integrated dataset from Bristol Myers Squibb. At the patient level, correlation between OS and PFS was consistently stronger than those between OS and best overall response (BOR), suggesting that PFS may serve as a more reliable surrogate for OS. Melanoma patients exhibited the highest correlation between OS and BOR, and immune-oncology (IO) therapy patients showed stronger correlations than those treated with chemotherapy. First-line therapy patients demonstrated stronger correlations between BOR, PFS, and OS compared with second-line or third-line patients. At the trial level, the correlation between PFS hazard ratio (HR) and difference in ORR (∆ORR) was stronger than that between other endpoints. Melanoma studies exhibited strong correlations with significant P-values. IO therapy studies exhibited more consistent correlations.

Pregnancy induces significant adaptations in the cardio-autonomic nervous system, with additional cardiac stress in preeclampsia potentially impacting ventricular repolarization. Despite the widespread use of QT-prolonging drugs during pregnancy, the extent of heart rate (HR)-corrected QT (QTc) interval changes during normal pregnancy and preeclampsia remains unclear. This study aimed to quantify changes in QTc interval across different trimesters of normal pregnancy and third-trimester preeclampsia. Eight databases were systematically searched from their inception to January 13, 2025. Any type of study design, except case reports/series, reporting QT interval and HR or RR interval, and/or QTc interval for at least one trimester were included. Those reporting at least two trimesters or one trimester with nonpregnant controls were pooled in meta-analyses using random-effect models to calculate pooled mean differences (MD) across trimesters. Data from 57 studies (6,686 participants) were included with 33 studies (5,153 participants) pooled in meta-analyses. Compared with nonpregnant individuals, QTc intervals increased across trimesters of normal pregnancy and in third-trimester preeclampsia. Meta-analyses revealed significant increases in QTc interval during first (MD = 10.0 msec), second (MD = 20.2 msec), and third trimesters (MD = 23.0 msec) compared with nonpregnant individuals. Furthermore, preeclampsia increased the QTc interval by 21.7 msec during the third trimester compared to normal pregnancy. No publication bias was detected, and the overall quality scores of most studies were fair (n = 23) or poor (n = 33). A significant QTc interval lengthening throughout normal pregnancy was identified, and to a greater extent during preeclampsia. The arrhythmogenicity in third-trimester preeclampsia with a known risk for QTc interval prolongation, especially with using QT-prolonging drugs, warrants further investigation.

Accurately assessing glomerular filtration rate (GFR) from plasma creatinine concentrations is challenging in patients with unstable renal function. This study aimed to refine the understanding of creatinine kinetics for more reliable assessments of GFR and net creatinine tubular secretion (nCTS) via OCT2/MATE in humans. In a clinical study of 14 healthy volunteers, iohexol was administered intravenously as a reference GFR marker, and creatinine was introduced through a meat meal. A joint pharmacometric model was developed using dense plasma and urine sampling. Simulations were used to evaluate the effect of different creatinine volume of distribution (V_d) values on GFR estimation after acute kidney injury (AKI) and to assess the impact of limited sampling strategies on GFR and nCTS estimation. Pharmacokinetic parameters for iohexol and creatinine aligned with reported values, but a lower V_d of 41% of total body weight and a nCTS fraction of 31% relative to overall creatinine clearance were observed. Commonly used equations based on single-point creatinine measurement all overestimated GFR, with the Modification of Diet in Renal Disease (MDRD) equation performing best, followed by Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2009 equation. Simulations demonstrate the effect of V_d estimate accuracy on detecting AKI from creatinine plasma concentrations only. Following low-dose iohexol administration, a single plasma sample at 5 hours and a urine sample from 0 to 5 hours provided accurate estimates of both GFR and nCTS using the joint model and enabled adequate correction for incomplete urine collection. This approach shows promise for assessing renal transporter activity based on estimated nCTS.