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Bandeira LC, Pinto L, Carneiro CM. Pharmacometrics: The Already-Present Future of Precision Pharmacology. Ther Innov Regul Sci 2023; 57:57-69. [PMID: 35984633 DOI: 10.1007/s43441-022-00439-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 07/20/2022] [Indexed: 02/01/2023]
Abstract
The use of mathematical modeling to represent, analyze, make predictions or providing information on data obtained in drug research and development has made pharmacometrics an area of great prominence and importance. The main purpose of pharmacometrics is to provide information relevant to the search for efficacy and safety improvements in pharmacotherapy. Regulatory agencies have adopted pharmacometrics analysis to justify their regulatory decisions, making those decisions more efficient. Demand for specialists trained in the field is therefore growing. In this review, we describe the meaning, history, and development of pharmacometrics, analyzing the challenges faced in the training of professionals. Examples of applications in current use, perspectives for the future, and the importance of pharmacometrics for the development and growth of precision pharmacology are also presented.
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Affiliation(s)
- Lorena Cera Bandeira
- Laboratory of Immunopathology, Nucleus of Biological Sciences Research, Federal University of Ouro Preto, Ouro Preto, Minas Gerais, Brazil.
| | - Leonardo Pinto
- Laboratory of Immunopathology, Nucleus of Biological Sciences Research, Federal University of Ouro Preto, Ouro Preto, Minas Gerais, Brazil
| | - Cláudia Martins Carneiro
- Laboratory of Immunopathology, Nucleus of Biological Sciences Research, Federal University of Ouro Preto, Ouro Preto, Minas Gerais, Brazil
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Kalaria SN, Farchione TR, Uppoor R, Mehta M, Wang Y, Zhu H. Extrapolation of Efficacy and Dose Selection in Pediatrics: A Case Example of Atypical Antipsychotics in Adolescents With Schizophrenia and Bipolar I Disorder. J Clin Pharmacol 2021; 61 Suppl 1:S117-S124. [PMID: 34185904 DOI: 10.1002/jcph.1836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 02/12/2021] [Indexed: 11/07/2022]
Abstract
Pediatric labeling information for novel atypical antipsychotics can be significantly delayed as the result of time lag between initial drug approval in adults and the completion of pediatric clinical trials. This delay can lead health care providers to rely on limited evidence-based literature to make critical therapeutic decisions for pediatric patients. Effective and scientifically justified dosing recommendations are needed to improve treatment outcomes in pediatric patients with schizophrenia and bipolar I disorder. Extrapolation-based drug development strategies rely on leveraging prior data to reduce evidentiary requirements for newer data in establishing drug efficacy. On January 13, 2020, the US Food and Drug Administration (FDA) released a general advice letter to sponsors highlighting the acceptance of extrapolating efficacy of atypical antipsychotics to pediatric patients. This review provides insight into the FDA's justification for extrapolating efficacy from adult to pediatric patients and provides a rationale for dose selection in pediatric patients with schizophrenia and bipolar I disorder.
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Affiliation(s)
- Shamir N Kalaria
- Division of Psychiatry, Office of New Drugs, Center for Drug Evaluation Research, White Oak, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Tiffany R Farchione
- Division of Psychiatry, Office of New Drugs, Center for Drug Evaluation Research, White Oak, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Ramana Uppoor
- Division of Neuropsychiatric Pharmacology, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation Research, US Food and Drug Administration, White Oak, Silver Spring, Maryland, USA
| | - Mehul Mehta
- Division of Neuropsychiatric Pharmacology, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation Research, US Food and Drug Administration, White Oak, Silver Spring, Maryland, USA
| | - Yaning Wang
- Division of Pharmacometrics, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation Research, US Food and Drug Administration, White Oak, Silver Spring, Maryland, USA
| | - Hao Zhu
- Division of Pharmacometrics, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation Research, US Food and Drug Administration, White Oak, Silver Spring, Maryland, USA
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Kalaria SN, McElroy SL, Gobburu J, Gopalakrishnan M. An Innovative Disease-Drug-Trial Framework to Guide Binge Eating Disorder Drug Development: A Case Study for Topiramate. Clin Transl Sci 2019; 13:88-97. [PMID: 31386273 PMCID: PMC6951469 DOI: 10.1111/cts.12682] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 07/01/2019] [Indexed: 11/27/2022] Open
Abstract
As with other psychiatric disorders, development of drugs to treat binge-eating disorder (BED) has been hampered by high placebo response and dropout rates in randomized controlled trials (RCTs). Although not approved for use in BED, several RCTs have suggested that topiramate is efficacious for BED in obese individuals. Using data from a positive investigator-initiated RCT of topiramate in 61 obese individuals with BED, the objective of the present study is (i) to develop a quantitative disease-drug-trial framework to inform future BED clinical trial designs, and (ii) to determine the optimal topiramate dose to achieve therapeutic efficacy. Disease-drug-trial models were developed separately for the two efficacy measures, namely, longitudinal normalized weekly binge-eating episode frequency (BEF) and binge day frequency (BDF). Model building consisted of (i) developing a placebo effect model that describes response from the placebo group, (ii) adding a drug effect to the placebo model to describe dose-response relationships, and (iii) developing a parametric time to event model to characterize patient dropout patterns. The placebo effect on normalized BEF and BDF over time demonstrated a maximum decrease of ~ 57% by 5 weeks. Participants had a higher dropout probability if no weight loss occurred during the trial period. The identified dose-response relationship demonstrated a daily dose of 125 mg was needed to exhibit a marked reduction in weekly BEF. The developed comprehensive disease-drug-trial model will be utilized to simulate different clinical trial designs to increase the success for future BED drug development programs.
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Affiliation(s)
- Shamir N Kalaria
- Center for Translational Medicine, University of Maryland School of Pharmacy, Baltimore, Maryland, USA
| | - Susan L McElroy
- Linder Center for HOPE, Mason Ohio and University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Jogarao Gobburu
- Center for Translational Medicine, University of Maryland School of Pharmacy, Baltimore, Maryland, USA
| | - Mathangi Gopalakrishnan
- Center for Translational Medicine, University of Maryland School of Pharmacy, Baltimore, Maryland, USA
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Kalaria SN, Zhu H, Farchione TR, Mathis MV, Gopalakrishnan M, Uppoor R, Mehta M, Younis I. A Quantitative Justification of Similarity in Placebo Response Between Adults and Adolescents With Acute Exacerbation of Schizophrenia in Clinical Trials. Clin Pharmacol Ther 2019; 106:1046-1055. [DOI: 10.1002/cpt.1501] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 04/23/2019] [Indexed: 01/18/2023]
Affiliation(s)
- Shamir N. Kalaria
- Center for Translational MedicineUniversity of Maryland School of Pharmacy Baltimore Maryland USA
| | - Hao Zhu
- Office of Clinical PharmacologyOffice of Translational ScienceCenter for Drug Evaluation ResearchUS Food and Drug Administration White Oak Maryland USA
| | - Tiffany R. Farchione
- Division of Psychiatry ProductsOffice of New DrugsCenter for Drug Evaluation ResearchUS Food and Drug Administration White Oak Maryland USA
| | - Mitchell V. Mathis
- Division of Psychiatry ProductsOffice of New DrugsCenter for Drug Evaluation ResearchUS Food and Drug Administration White Oak Maryland USA
| | - Mathangi Gopalakrishnan
- Center for Translational MedicineUniversity of Maryland School of Pharmacy Baltimore Maryland USA
| | - Ramana Uppoor
- Office of Clinical PharmacologyOffice of Translational ScienceCenter for Drug Evaluation ResearchUS Food and Drug Administration White Oak Maryland USA
| | - Mehul Mehta
- Office of Clinical PharmacologyOffice of Translational ScienceCenter for Drug Evaluation ResearchUS Food and Drug Administration White Oak Maryland USA
| | - Islam Younis
- Office of Clinical PharmacologyOffice of Translational ScienceCenter for Drug Evaluation ResearchUS Food and Drug Administration White Oak Maryland USA
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Wang Y, Zhu H, Madabushi R, Liu Q, Huang S, Zineh I. Model‐Informed Drug Development: Current US Regulatory Practice and Future Considerations. Clin Pharmacol Ther 2019; 105:899-911. [DOI: 10.1002/cpt.1363] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 12/26/2018] [Indexed: 11/10/2022]
Affiliation(s)
- Yaning Wang
- Office of Clinical PharmacologyOffice of Translational SciencesUS Food and Drug Administration Silver Spring Maryland USA
| | - Hao Zhu
- Office of Clinical PharmacologyOffice of Translational SciencesUS Food and Drug Administration Silver Spring Maryland USA
| | - Rajanikanth Madabushi
- Office of Clinical PharmacologyOffice of Translational SciencesUS Food and Drug Administration Silver Spring Maryland USA
| | - Qi Liu
- Office of Clinical PharmacologyOffice of Translational SciencesUS Food and Drug Administration Silver Spring Maryland USA
| | - Shiew‐Mei Huang
- Office of Clinical PharmacologyOffice of Translational SciencesUS Food and Drug Administration Silver Spring Maryland USA
| | - Issam Zineh
- Office of Clinical PharmacologyOffice of Translational SciencesUS Food and Drug Administration Silver Spring Maryland USA
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Isoherranen N, Madabushi R, Huang S. Emerging Role of Organ-on-a-Chip Technologies in Quantitative Clinical Pharmacology Evaluation. Clin Transl Sci 2019; 12:113-121. [PMID: 30740886 PMCID: PMC6440571 DOI: 10.1111/cts.12627] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 01/26/2019] [Indexed: 12/28/2022] Open
Abstract
The recently enacted Prescription Drug User Fee Act (PDUFA) VI includes in its performance goals "enhancing regulatory science and expediting drug development." The key elements in "enhancing regulatory decision tools to support drug development and review" include "advancing model-informed drug development (MIDD)." This paper describes (i) the US Food and Drug Administration (FDA) Office of Clinical Pharmacology's continuing efforts in developing quantitative clinical pharmacology models (disease, drug, and clinical trial models) to advance MIDD, (ii) how emerging novel tools, such as organ-on-a-chip technologies or microphysiological systems, can provide new insights into physiology and disease mechanisms, biomarker identification and evaluation, and elucidation of mechanisms of adverse drug reactions, and (iii) how the single organ or linked organ microphysiological systems can provide critical system parameters for improved physiologically-based pharmacokinetic and pharmacodynamic evaluations. Continuous public-private partnerships are critical to advance this field and in the application of these new technologies in drug development and regulatory review.
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Affiliation(s)
- Nina Isoherranen
- Office of Clinical Pharmacology (OCP)Office of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
- Department of PharmaceuticsSchool of PharmacyUniversity of WashingtonSeattleWashingtonUSA
| | - Rajanikanth Madabushi
- Office of Clinical Pharmacology (OCP)Office of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
| | - Shiew‐Mei Huang
- Office of Clinical Pharmacology (OCP)Office of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
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Madrasi K, Li F, Kim MJ, Samant S, Voss S, Kehoe T, Bashaw ED, Ahn HY, Wang Y, Florian J, Schmidt S, Lesko LJ, Li L. Regulatory Perspectives in Pharmacometric Models of Osteoporosis. J Clin Pharmacol 2018; 58:572-585. [PMID: 29485684 DOI: 10.1002/jcph.1071] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2017] [Accepted: 11/24/2017] [Indexed: 11/12/2022]
Abstract
Osteoporosis is a disorder of the bones in which they are weakened to the extent that they become more prone to fracture. There are various forms of osteoporosis: some of them are induced by drugs, and others occur as a chronic progressive disorder as an individual gets older. As the median age of the population rises across the world, the chronic form of the bone disease is drawing attention as an important worldwide health issue. Developing new treatments for osteoporosis and comparing them with existing treatments are complicated processes due to current acceptance by regulatory authorities of bone mineral density (BMD) and fracture risk as clinical end points, which require clinical trials to be large, prolonged, and expensive to determine clinically significant impacts in BMD and fracture risk. Moreover, changes in BMD and fracture risk are not always correlated, with some clinical trials showing BMD improvement without a reduction in fractures. More recently, bone turnover markers specific to bone formation and resorption have been recognized that reflect bone physiology at a cellular level. These bone turnover markers change faster than BMD and fracture risk, and mathematically linking the biomarkers via a computational model to BMD and/or fracture risk may help in predicting BMD and fracture risk changes over time during the progression of a disease or when under treatment. Here, we discuss important concepts of bone physiology, osteoporosis, treatment options, mathematical modeling of osteoporosis, and the use of these models by the pharmaceutical industry and the Food and Drug Administration.
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Affiliation(s)
- Kumpal Madrasi
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Fang Li
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Myong-Jin Kim
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Snehal Samant
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, University of Florida, Gainesville, FL, USA
| | - Stephen Voss
- Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Theresa Kehoe
- Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - E Dennis Bashaw
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Hae Young Ahn
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Yaning Wang
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Jeffy Florian
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Stephan Schmidt
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, University of Florida, Gainesville, FL, USA
| | - Lawrence J Lesko
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, University of Florida, Gainesville, FL, USA
| | - Li Li
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
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Venuto CS, Potter NB, Dorsey ER, Kieburtz K. A review of disease progression models of Parkinson's disease and applications in clinical trials. Mov Disord 2016; 31:947-956. [PMID: 27226141 PMCID: PMC4931998 DOI: 10.1002/mds.26644] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Revised: 02/19/2016] [Accepted: 03/04/2016] [Indexed: 12/31/2022] Open
Abstract
Quantitative disease progression models for neurodegenerative disorders are gaining recognition as important tools for drug development and evaluation. In Parkinson's disease (PD), several models have described longitudinal changes in the Unified Parkinson's Disease Rating Scale (UPDRS), one of the most utilized outcome measures for PD trials assessing disease progression. We conducted a literature review to examine the methods and applications of quantitative disease progression modeling for PD using a combination of key words including "Parkinson disease," "progression," and "model." For this review, we focused on models of PD progression quantifying changes in the total UPDRS scores against time. Four different models reporting equations and parameters have been published using linear and nonlinear functions. The reasons for constructing disease progression models of PD thus far have been to quantify disease trajectories of PD patients in active and inactive treatment arms of clinical trials, to quantify and discern symptomatic and disease-modifying treatment effects, and to demonstrate how model-based methods may be used to design clinical trials. The historical lack of efficiency of PD clinical trials begs for model-based simulations in planning for studies that result in more informative conclusions, particularly around disease modification. © 2016 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Charles S. Venuto
- Center for Human Experimental Therapeutics, University of Rochester, Rochester, NY, USA
- Department of Neurology, University of Rochester, Rochester NY USA
| | - Nicholas B. Potter
- Center for Human Experimental Therapeutics, University of Rochester, Rochester, NY, USA
| | - E. Ray Dorsey
- Center for Human Experimental Therapeutics, University of Rochester, Rochester, NY, USA
- Department of Neurology, University of Rochester, Rochester NY USA
| | - Karl Kieburtz
- Center for Human Experimental Therapeutics, University of Rochester, Rochester, NY, USA
- Department of Neurology, University of Rochester, Rochester NY USA
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Pharmacokinetic-pharmacodynamic modeling of the antitumor effect of TM208 and EGFR-TKI resistance in human breast cancer xenograft mice. Acta Pharmacol Sin 2016; 37:825-33. [PMID: 27133303 DOI: 10.1038/aps.2016.40] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Accepted: 01/09/2016] [Indexed: 12/16/2022] Open
Abstract
AIM The novel anticancer compound TM208 is an EGFR tyrosine kinase inhibitor (EGFR-TKI). Since the development of resistance to EGFR-TKIs is a major challenge in their clinical usage, we investigated the profiles of resistance following continuous treatment with TM208 in human breast cancer xenograft mice, and identified the relationship between the tumor pEGFR levels and tumor growth inhibition. METHODS Female BALB/c nude mice were implanted with human breast cancer MCF-7 cells, and the xenograft mice received TM208 (50 or 150 mg·kg(-1)·d(-1), ig) or vehicle for 18 d. The pharmacokinetics (PK) and pharmacodynamics (PD) of TM208 were evaluated. RESULTS The PK properties of TM208 were described by a one-compartment model with first-order absorption kinetics. Our study showed the inhibitory effects of TM208 on tumor pEGFR levels gradually reached a maximum effect, after which it became weaker over time, which was characterized by a combined tolerance/indirect response PD model with an estimated EC50 (55.9 μg/L), as well as three parameters ('a' of 27.2%, 'b' of 2730%, 'c' of 0.58 h(-1)) denoting the maximum, extent and rate of resistance, respectively. The relationship between the tumor pEGFR levels and tumor growth inhibition was characterized by a combined logistic tumor growth/transit compartment model with estimated parameters associated with tumor growth characteristics kng (0.282 day(-1)), drug potency kTM208 (0.0499 cm(3)/day) and the kinetics of tumor cell death k1 (0.141 day(-1)), which provided insight into drug mechanisms and behaviors. CONCLUSION The proposed PK/PD model provides a better understanding of the pharmacological properties of TM208 in the treatment of breast cancer. Furthermore, simulation based on a tolerance model allows prediction of the occurrence of resistance.
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Welten CCM, Koeter MWJ, Wohlfarth T, Storosum JG, van den Brink W, Gispen-de Wied CC, Leufkens HGM, Denys DAJP. Placebo response in antipsychotic trials of patients with acute mania: Results of an individual patient data meta-analysis. Eur Neuropsychopharmacol 2015; 25:1018-26. [PMID: 25907248 DOI: 10.1016/j.euroneuro.2015.03.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2014] [Revised: 02/05/2015] [Accepted: 03/22/2015] [Indexed: 11/17/2022]
Abstract
We examined the role of placebo response in acute mania trials. Specifically, whether placebo response: (1) predicts treatment effect, (2) can be predicted by patient and study characteristics, and (3) can be predicted by a parsimonious model. We performed a meta-analysis of individual patient data from 10 registration studies (n=1019) for the indication acute manic episode of bipolar disorder. We assessed the effect of 14 determinants on placebo response. Primary outcome measures were mean symptom change score (MCS) on the Young Mania Rating Scale (YMRS) and response rate (RR), defined as ≥ 50% YMRS symptom improvement from baseline to endpoint. The overall placebo response was 8.5 points improvement on the YMRS (=27.9%) with a RR of 32.8%. Placebo response was significantly associated with the overall treatment response. Five determinants significantly (p<0.05) predicted the placebo response. The multivariate prediction model, which consisted of baseline severity, psychotic features at baseline, number of geographic regions, and region, explained 10.4% and 5.5% of the variance in MSC and RR, respectively. Our findings showed that the placebo response in efficacy trials of antipsychotics for acute mania is substantial and an important determinant of treatment effect. Placebo response is influenced by patient characteristics (illness severity and presence of psychotic features) and by study characteristics (study year, number of geographic regions and region). However, the prediction model could only explain the placebo response to a limited extent. Therefore, limiting trials to certain patients in certain geographic regions seems not a viable strategy to improve assay sensitivity.
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Affiliation(s)
- C C M Welten
- Dept. of Psychiatry, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands; Medicines Evaluation Board, Utrecht, The Netherlands.
| | - M W J Koeter
- Dept. of Psychiatry, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - T Wohlfarth
- Medicines Evaluation Board, Utrecht, The Netherlands
| | - J G Storosum
- Dept. of Psychiatry, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - W van den Brink
- Dept. of Psychiatry, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | | | | | - D A J P Denys
- Dept. of Psychiatry, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands; Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
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Levine SZ, Goldberg Y, Samara M, Davis JM, Leucht S. Joint modeling of dropout and outcome in three pivotal clinical trials of schizophrenia. Schizophr Res 2015; 164:122-6. [PMID: 25790904 DOI: 10.1016/j.schres.2015.02.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2014] [Revised: 02/05/2015] [Accepted: 02/25/2015] [Indexed: 11/29/2022]
Abstract
BACKGROUND Dropout is a serious challenge to clinical trials in psychiatry, yet standard outcome analyses with mixed models do not account for dropout, while joint modeling uses dropout from a survival model to adjust the outcome from a mixed model, but is untested in clinical trials of schizophrenia. AIMS To compare mixed and joint modeling in three acute phase pivotal placebo controlled trials of schizophrenia. METHOD Data were reanalyzed on 611 in-patients with acute schizophrenia who participated in three pivotal randomized controlled trials that compared placebo with olanzapine or risperidone (dropout rates placebo: 62.6% and medication: 37.4%). The outcome measures were BPRS or PANSS total change scores. Mixed-effects models for repeated measures and joint models were computed and compared to examine the time-treatment interaction. Effect size comparisons were made. RESULTS Antipsychotic treatment was superior to placebo across analyses. Time treatment interactions were significant (p<.05) for the mixed (beta=2.33) and joint models (beta=2.62). Compared with mixed modeling, joint modeling reduced the estimated change score for treatment (21.24 vs 19.74) and placebo (1.64 vs -1.11). The effect size differences between placebo and treatment groups were greater for joint (ES=.89) than mixed modeling (ES=0.83). Sensitivity analysis replicated this trend of results in each of the three trials. CONCLUSION Compared to mixed modeling, joint modeling results in a greater separation between treatment and placebo groups. This offers preliminary evidence that joint modeling may be useful in the analysis of antipsychotic placebo controlled RCTs.
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Affiliation(s)
| | | | - Myrto Samara
- Department of Psychiatry and Psychotherapy, Technische Universität München, Germany
| | - John M Davis
- Psychiatric Institute, University of Illinois at Chicago, USA
| | - Stefan Leucht
- Department of Psychiatry and Psychotherapy, Technische Universität München, Germany
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