Pharmacotherapy最新文献

{"title":"Correction to \"Pharmacokinetics of Ceftolozane/Tazobactam in Patients With Partial-and Full-Thickness Skin Burns\".","authors":"","doi":"10.1002/phar.70091","DOIUrl":"10.1002/phar.70091","url":null,"abstract":"","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70091"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145805129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Pharmacological Treatment Response in Migraine Using AI/ML: A Scoping Review of the Evidence and Future Directions.","authors":"Martina Giacon, Salvatore Terrazzino","doi":"10.1002/phar.70085","DOIUrl":"10.1002/phar.70085","url":null,"abstract":"<p><p>The treatment of migraine is hampered by inter-individual variability, leading to an inefficient \"trial and error\" approach. Artificial intelligence (AI) and machine learning (ML) offer a path towards precision medicine by predicting therapeutic outcomes. This scoping review systematically evaluates the evidence for AI and ML models for predicting pharmacologic response in migraine. A systematic search of four databases (PubMed, Web of Knowledge, Cochrane Library, and OpenGrey) identified 12 eligible studies using AI/ML to predict acute or prophylactic response to migraine treatment. These studies, which date back to articles published in 2006 and have been increasingly published recently, used a wide range of methods, from classical algorithms like support vector machines to deep learning and probabilistic models. The models primarily utilized clinical phenotyping and neuroimaging data and reported high predictive accuracy for novel biologics (e.g., anti-calcitonin gene-related peptide monoclonal antibodies (CGRP mAbs)) and acute treatments (e.g., nonsteroidal anti-inflammatory drugs (NSAIDs)). However, our systematic review finds that this apparent success is undermined by critical and pervasive methodological weaknesses. The central finding is that most studies relied solely on internal validation, carrying a high risk of overfitting, with external validation being exceptionally rare. Furthermore, several publications were based on overlapping patient cohorts, and a complete lack of biomarker or genetic data was noted. Consequently, the clinical application of AI and ML is currently stalled. Future progress depends on overcoming the \"crisis of generalizability\" by mandating external validation, addressing the \"data bottleneck\" with large, diverse datasets, and expanding data modalities to include \"omic\" data. These measures are critical to begin to realize the potential of AI and ML to personalize migraine treatment and significantly improve patient outcomes.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70085"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12862525/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145588230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Testosterone Therapy on Cytochrome P450 3A and P-Glycoprotein Activities Using Midazolam and Digoxin as Probe Substrates Among Transgender Adults.","authors":"Michiko Hunter, Rene Coig, Linda Risler, Kristen K Patton, Radhika R Narla, Dina N Greene, Alson K Burke, Elizabeth Micks, Mary F Hebert, Lauren R Cirrincione","doi":"10.1002/phar.70093","DOIUrl":"10.1002/phar.70093","url":null,"abstract":"<p><strong>Background: </strong>Gender-affirming testosterone therapy is one part of the standard of care for more than 1 million transgender adults in the United States. Testosterone therapy may influence the activities of drug-metabolizing enzymes and transporters, but knowledge about its effect on the pharmacokinetics of other medications is limited. We determined the effects of gender-affirming testosterone therapy on apparent cytochrome P450 (CYP) 3A and P-glycoprotein activities using midazolam and digoxin as model probe substrates among transgender adults.</p><p><strong>Methods: </strong>This was a longitudinal (pre-treatment and with concomitant testosterone therapy), prospective, non-randomized, open-label, three-phase probe substrate study. Eligible participants started testosterone therapy based on clinical need. Participants received one oral dose of midazolam 2 mg and digoxin 0.25 mg (simultaneous dosing) under fasted conditions before starting gender-affirming testosterone therapy (baseline), and at 1-month and 3-months on gender-affirming testosterone therapy. Midazolam, 1'-hydroxymidazolam, 4-hydroxymidazolam, digoxin, and total testosterone concentrations were determined by liquid chromatography-tandem mass spectrometry assays. We estimated single-dose pharmacokinetic parameters of midazolam, its metabolites, and digoxin using standard noncompartmental methods. Pharmacokinetic parameters were compared with testosterone therapy at 1-month and 3-months to baseline as geometric mean ratios (90% confidence intervals) and paired t-tests after log transformation. A p < 0.025 was considered significant.</p><p><strong>Results: </strong>Among 14 participants (mean age: 24 ± 3 years; weight: 82.9 ± 20.9 kg; race/ethnicity: 71% White, non-Hispanic, 14% Hispanic, 7% Asian, 7% mixed race), nine participants started weekly testosterone injections (20 mg to 80 mg once weekly) and five started daily transdermal testosterone applications (12.5 mg to 50 mg once daily gel or cream, 2 mg daily patch). Mean total testosterone concentrations at 3 months increased more than 20-fold from baseline concentrations (25 ± 7 ng/dL to 507 ± 263 ng/dL). Geometric mean midazolam and metabolite pharmacokinetic parameters and digoxin parameters were not significantly different at baseline and with testosterone therapy.</p><p><strong>Conclusion: </strong>Gender-affirming testosterone therapy did not significantly affect CYP3A or P-glycoprotein activities. Gender-affirming testosterone therapy may have minimal effects on the pharmacokinetics of other medications that are substrates of CYP3A and P-glycoprotein. Caution may be warranted for medications with a narrow therapeutic index.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70093"},"PeriodicalIF":4.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12863264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145843890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cancer Therapy-Induced Cardiotoxicity: A Narrative Review.","authors":"Drew A Wells, Zachery Halford, Kori Holman, Dennis Marjoncu","doi":"10.1002/phar.70110","DOIUrl":"10.1002/phar.70110","url":null,"abstract":"<p><p>Cancer therapy-induced cardiotoxicity (CTIC) is a significant clinical challenge in an era of rapidly evolving oncologic treatment. This comprehensive narrative review synthesizes evidence from clinical trials, meta-analyses, and guidelines to examine the definitions, mechanisms, offending agents, and preventative strategies for CTIC. Numerous therapeutic agents, beyond traditional chemotherapy, pose significant risks for cardiovascular events such as arrhythmias, hypertension, myocardial dysfunction, and vascular complications. These varied toxicity profiles necessitate individualized monitoring strategies. Given the rising cancer incidence and the proliferation of novel oral chemotherapeutic agents taken at home, non-oncology practitioners are increasingly likely to encounter patients with these cardiotoxicities. This creates a critical need for broader clinical familiarity with CTIC mechanisms and cardiovascular optimization strategies to mitigate long-term morbidity. Although existing evidence helps guide risk stratification, significant questions remain. Robust data from dedicated cardio-oncology trials, coupled with enhanced multidisciplinary collaboration, are urgently needed to define optimal prevention and treatment of CTIC for patients experiencing these serious adverse effects.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 2","pages":"e70110"},"PeriodicalIF":4.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148006266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Algorithms for Predicting Injurious Fall Risk Among Older Adults With Depression: A Prognostic Modeling Study.","authors":"Grace Hsin-Min Wang, Yao-An Lee, Amie J Goodin, Rachel C Reise, Ronald I Shorr, Wei-Hsuan Lo-Ciganic","doi":"10.1002/phar.70087","DOIUrl":"10.1002/phar.70087","url":null,"abstract":"<p><strong>Background: </strong>Falls and related injuries (FRI) pose a large burden among older adults with depression. Proactively identifying individuals at high FRI risk enables timely and tailored interventions, reducing unnecessary health care resource utilization. However, prior prediction models relied on fixed time intervals and failed to capture dynamic changes in health status over time.</p><p><strong>Objectives: </strong>To develop and validate machine-learning algorithms (i.e., elastic net, random forest, and gradient boosting machine) for predicting 3-month FRI risk among older adults with depression.</p><p><strong>Methods: </strong>This prognostic modeling study included fee-for-service Medicare beneficiaries aged 65 years or older with a depression diagnosis in 2017. Beneficiaries were followed in 3-month episodes from the first depression diagnosis until the earliest of death, hospice services or nursing facility utilization, switching to Medicare Advantage plans, or the end of the study period (i.e., December 31, 2019). A total of 261 time-varying predictors, spanning patient-, provider-, health system- and region-related factors, were updated every 3 months to predict incident FRI risk in the subsequent 3 months. We assessed prediction performance using c-statistics and stratified patients into different risk subgroups using the best-performing model.</p><p><strong>Results: </strong>Among 274,268 eligible beneficiaries, the mean age was 74.6 (standard deviation [SD] = 7.2) years, 32.0% were male, 85.2% were White, and 15.1% experienced at least one FRI event throughout the study period. Using the random forest model (c-statistics = 0.68), 68.9% of the actual FRI cases were captured in the top three deciles of predicted risk. Individuals in the bottom seven deciles had a minimal FRI incidence (< 1.7%). Key predictors included frailty, age, prior FRI history, and daily dose of antidepressants.</p><p><strong>Conclusion: </strong>Using a nationally representative cohort and time-varying predictors, our model offers a practical approach for efficiently identifying older adults at high FRI risk, which can be updated over time. This approach can inform clinical decision-making and optimize the allocation of fall prevention resources.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70087"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145637552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of Serotonin Transporter Gene 5HTTLPR rs25531 Polymorphism With Escitalopram Efficacy in Patients With Major Depressive Disorder.","authors":"Muhammad Faisal Afridi, Shahbaz Ahmad Zakki, Rafiullah Rafiullah, Ijaz Ul Haq","doi":"10.1002/phar.70098","DOIUrl":"https://doi.org/10.1002/phar.70098","url":null,"abstract":"<p><strong>Background: </strong>Escitalopram is considered a cornerstone therapy for major depression, but inter-individual variations exist in its clinical efficacy. The current study aims to explore the possible association between serotonin transporter gene (SLC6A4) polymorphism 5HTTLPR-rs25531 and clinical efficacy of escitalopram in a Pakistani population suffering from major depressive disorder.</p><p><strong>Methodology: </strong>This prospective observational analytical study was conducted from April 2023 to October 2024 in tertiary care hospitals. Four hundred and thirty depressive patients were recruited through non-probability purposive sampling. Patients were treated with escitalopram 10 mg once daily for 12 weeks. Patients were enrolled based on Hamilton Depression Rating Scale score of less than or equal to 20. Patients with a minimum 50% reduction in score at 12 weeks were designated as responders. Genotyping of 5-HTTLPR- rs25531 was performed by allele-specific polymerase chain reaction.</p><p><strong>Results: </strong>Responders and non-responders of escitalopram therapy were 217 (50.50%) and 213 (49.5%) patients, respectively. Frequencies of LL, LS, and SS genotypes of HTTLPR polymorphisms were 19%, 47%, and 34%, respectively, and frequencies of L and S alleles were 0.43 and 0.57, respectively. A higher percentage reduction in the Hamilton Depression Rating Scale was observed in patients carrying LL (60.05% ± 21.24%) and LS (53.63% ± 22.8%) genotypes compared to those having the SS genotype (32.22% ± 16.59%).</p><p><strong>Conclusion: </strong>5HTTLPR and rs-25,531 polymorphisms significantly influenced escitalopram efficacy, and patients carrying LL genotypes with L_AL_A haplotype and 2 L functional group have better treatment response to escitalopram therapy compared to other groups.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 2","pages":"e70098"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148006225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of a Novel Scoring Model Integrating Clinical Risk Factors and Pharmacokinetic Parameters to Predict Vancomycin-Induced Nephrotoxicity.","authors":"Yoshihiko Matsuki, Ken-Ichi Sako, Yutaro Kozima, Tamaki Watanabe, Yasuharu Kashiwagura, Nobuhiro Yasuno, Shigekazu Watanabe","doi":"10.1002/phar.70111","DOIUrl":"10.1002/phar.70111","url":null,"abstract":"<p><strong>Background: </strong>Vancomycin (VCM), a first-line treatment option for infections caused by methicillin-resistant Staphylococcus aureus, has been reported to cause nephrotoxicity even within therapeutic concentration ranges. Traditional therapeutic drug monitoring strategies rely primarily on the area under the concentration-time curve (AUC), without adequately accounting for multiple clinical risk factors associated with nephrotoxicity.</p><p><strong>Objective: </strong>The present study aimed to develop a novel scoring model that integrates clinical risk factors and pharmacokinetic parameters to predict VCM-induced nephrotoxicity and validate its predictive performance.</p><p><strong>Methods: </strong>We conducted a single-center retrospective cohort study on patients who received VCM therapy between April 2021 and March 2023. A multivariable logistic regression analysis was performed to identify independent risk factors for VCM-induced nephrotoxicity, and regression coefficients were used to construct the scoring model. The predictive performance of the proposed model was compared with a conventional AUC-based model using the area under the receiver operating characteristic curve (ROC AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).</p><p><strong>Results: </strong>The scoring model consisted of the following components: the steady-state area under the concentration-time curve (0-8 points), the concomitant use of tazobactam/piperacillin (2 points), the use of loop diuretics (1 point), and the presence of chronic liver disease (2 points). The proposed model demonstrated high predictive performance, with ROC AUC values of 0.79 (95% confidence interval [CI]: 0.71-0.87) in the derivation cohort and 0.84 (95% CI: 0.72-0.96) in the validation cohort. Furthermore, the proposed model showed significantly better performance than the conventional model in terms of NRI (derivation cohort: 0.78, 95% CI: 0.47-1.08; validation cohort: 1.23, 95% CI: 0.79-1.67) and IDI (derivation cohort: 0.07, 95% CI: 0.04-0.11; validation cohort: 0.27, 95% CI: 0.15-0.39) (p < 0.001).</p><p><strong>Conclusion: </strong>The scoring model developed in the present study may enhance risk stratification for VCM-induced nephrotoxicity and contribute to advances in individualized dosing strategies in clinical practice.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 2","pages":"e70111"},"PeriodicalIF":4.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12852064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146093727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a Statistical Modeling-Based Machine Learning Approach for Capturing Drug Dosing Using a Proton Pump Inhibitor Case.","authors":"Amanda Massmann, Jordan F Baye, Max Weaver","doi":"10.1002/phar.70083","DOIUrl":"10.1002/phar.70083","url":null,"abstract":"<p><strong>Objective: </strong>To develop a statistical model to capture medication dosing for proton pump inhibitors (PPIs) using structured data from electronic health records (EHR).</p><p><strong>Methods: </strong>Medication data for PPIs was extracted from a single health care system EHR to develop a statistical model. Nearly 20 years' worth of PPI prescriptions were extracted and 25% of unique dosing regimens were manually labeled by two clinical pharmacists. Several machine learning models were trained and evaluated to predict dose. Training was applied to 70% of the unique dosing regimens. The remaining unique dosing regimens were tested and validated with standard regression metrics: root mean squared error (RMSE) and R-squared.</p><p><strong>Results: </strong>A total of 17,271 distinct patients had orders for a PPI comprising 186,801 unique PPI orders. Distinct pairs built on medication descriptions and SIG combinations resulted in 10,739 unique entities. Clinical pharmacists manually labeled 2679 examples for medication entity extraction. Regression metrics (R-squared, RMSE) were chosen as metrics to evaluate model performance. A stacked ensembled model proved to have the best results with a 0.09 RMSE and an R-squared of 0.825.</p><p><strong>Conclusion: </strong>The development of a statistical model to capture PPI dosing for both maintenance and complex dosing strategies was highly sensitive and accurate. A supervised learning prediction model helps overcome challenges in medication dosing identification by addressing concerns related to variability and complexity. Future strategies should focus on integrating unstructured data within the algorithm to further refine medication dosing capture.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70083"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145654937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Circulating Profiles of the Bile Acid Metabolomics in Patients With Polycystic Ovary Syndrome Treated With Metformin or Canagliflozin.","authors":"Qi Yan, Meili Cai, Nan Wang, Wenhao Wu, Hua Zhang, Yikun Zhu, Jing Luo, Manna Zhang, Jin Li","doi":"10.1002/phar.70092","DOIUrl":"10.1002/phar.70092","url":null,"abstract":"<p><strong>Objective: </strong>Bile acids are indispensable modulators in the development of polycystic ovary syndrome (PCOS). Our previous study identified that metformin and canagliflozin have similar efficacy in patients with PCOS combined with insulin resistance (IR). However, the effect of metformin or canagliflozin on bile acid metabolism in patients with PCOS has not been elucidated. The objective of this study was to use targeted metabolomics technology to compare alterations of circulating bile acid metabolites in patients with PCOS before and after treatment with metformin or canagliflozin.</p><p><strong>Design and patients: </strong>This study was a subanalysis of a previous randomized open-label study, in which patients with PCOS combined with IR were enrolled and treated with either metformin (n = 35) or canagliflozin (n = 33) for 12 weeks.</p><p><strong>Measurements: </strong>The serum bile acid profile was measured using high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). The differences in serum bile acid metabolites in patients with PCOS before and after treatment were analyzed. In addition, the correlation between bile acid metabolites and PCOS-related clinical characteristics was evaluated.</p><p><strong>Results: </strong>There were no significant differences in serum bile acid metabolites in patients with PCOS before and after canagliflozin treatment. Metformin treatment substantially decreased serum total bile acid levels in patients with PCOS, especially primary conjugated bile acids. The levels of taurochenodeoxycholic acid (TCDCA), glycocholic acid (GCA), and glycochenodeoxycholic acid (GCDCA) showed significant differences from baseline in the serum of patients with PCOS after treatment with metformin. Correlation analysis showed that alterations of GCA, TCDCA, and GCDCA were associated with changes in multiple clinical parameters of patients with PCOS treated with metformin.</p><p><strong>Conclusion: </strong>The effects of metformin and canagliflozin on bile acids metabolism in patients with PCOS are different. The beneficial effects of metformin on PCOS may be related to the changes in bile acid metabolites.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov: NCT04700839.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70092"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12862559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145768820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constructing a Personalized Treatment Rule for Initial Therapy in Early Parkinson's Disease.","authors":"Zachary P Brehm, Ruth B Schneider, Charles S Venuto, Greta Smith, Cuong Tuan Pham, Michael P McDermott, Ashkan Ertefaie","doi":"10.1002/phar.70069","DOIUrl":"10.1002/phar.70069","url":null,"abstract":"<p><strong>Background: </strong>Dopaminergic therapies such as levodopa and dopamine receptor agonists (DRA) improve motor function in people with Parkinson's disease. These therapies are also linked to the advent of motor complications such as dyskinesias and wearing-off episodes.</p><p><strong>Objectives: </strong>We illustrate a method that creates a personalized treatment rule that takes patient-specific information and provides a recommended first-line therapy for Parkinson's disease that will provide the best mean improvement in motor function while constraining the probability of a motor complication within the first 2 years of therapy below a level mutually deemed to be the maximum acceptable risk by the patient and clinician.</p><p><strong>Methods: </strong>We apply a machine learning technique that simultaneously optimizes for benefit and risk outcomes to a harmonized clinical dataset based on the CALM-PD and STEADY-PD III randomized clinical trials. This generates a decision rule for allocating patients to levodopa or a DRA, based on a specified risk threshold. We evaluate the individualized decision rule by comparing the mean benefit and risk outcomes under the decision rule to the mean outcomes from policies that assign all patients to either levodopa or a DRA.</p><p><strong>Results: </strong>The optimal decision rule improves the mean change from baseline in MDS-UPDRS (Movement Disorder Society Unified Parkinson's Disease Rating Scale) motor (Part 3) score compared to assigning all patients to a DRA and provides a smaller mean probability of motor complications than assigning all patients to levodopa. More data are required to further develop and validate this decision rule.</p><p><strong>Conclusions: </strong>An optimal decision rule can provide improved data adaptive treatment decisions that balance benefit and risk outcomes given a maximum acceptable risk.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70069"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12824558/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145637498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}