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Ayoun Alsoud R, Le Moan N, Holten-Andersen L, Knudsen T, Lennernäs H, Simonsson USH. Model-Based Interspecies Scaling for Predicting Human Pharmacokinetics of CB 4332, a Complement Factor I Protein. J Pharm Sci 2024; 113:2895-2903. [PMID: 38945365 DOI: 10.1016/j.xphs.2024.06.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 06/25/2024] [Accepted: 06/25/2024] [Indexed: 07/02/2024]
Abstract
Interspecies scaling of the pharmacokinetics (PK) of CB 4332, a 150 kDa recombinant complement factor I protein, was performed using traditional and model-based approaches to inform first-in-human dose selection. Plasma concentration versus time data from four preclinical PK studies of single intravenous and subcutaneous (SC) CB 4332 dosing in mice, rats and nonhuman primates (NHPs) were modeled simultaneously using naive pooling including allometric scaling. The human-equivalent dose was calculated using the preclinical no observed adverse effect level (NOAEL) as part of the dose-by-factor approach. Pharmacokinetic modeling of CB 4332 revealed species-specific differences in the elimination, which was accounted for by including an additional rat-specific clearance. Signs of anti-drug antibodies (ADA) formation in all rats and some NHPs were observed. Consequently, an additional ADA-induced clearance parameter was estimated including the time of onset. The traditional dose-by-factor approach calculated a maximum recommended starting SC dose of 0.9 mg/kg once weekly, which was predicted it to result in a trough steady-state concentration lower than the determined efficacy target range for CB 4332 in humans. Model simulations predicted the efficacy target range to be reached using 5 mg/kg once weekly SC dosing.
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Affiliation(s)
- Rami Ayoun Alsoud
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | | | | | - Tom Knudsen
- Catalyst Biosciences, South San Francisco, California, USA
| | - Hans Lennernäs
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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Fayette L, Leroux R, Mentré F, Seurat J. Robust and Adaptive Two-stage Designs in Nonlinear Mixed Effect Models. AAPS J 2023; 25:71. [PMID: 37466809 DOI: 10.1208/s12248-023-00810-9] [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: 01/16/2023] [Accepted: 04/06/2023] [Indexed: 07/20/2023] Open
Abstract
To get informative studies for nonlinear mixed effect models (NLMEM), design optimization can be performed based on Fisher Information Matrix (FIM) using the D-criterion. Its computation requires knowledge about models and parameters, which are often prior guesses. Thus, adaptive designs composed of several stages may be used. Robust approach can also be used to account for various candidate models. In the estimation step of a given stage, model selection (MS) or model averaging (MA) can be performed. In this work we propose a new two-stage adaptive design strategy, based on the robust expected FIM and MA over several candidate models. The methodology is applied to a clinical trial simulation in ophthalmology to optimize doses and time measurements. A set of dose-response candidate models is defined, and one-stage designs are compared to two-stage 50/50 designs (i.e., each stage performed with half of the available subjects), using either local optimal design or robust design, and performing analysis with one model, MS or MA. Performing a two-stage design with MS at the interim analysis can correct the choice of a wrong model for designing the first stage. Overall, starting from a robust design (1- or 2-stage) is valuable and leads to reasonable bias and precision. The proposed robust adaptive design strategy is a new tool to design longitudinal studies that could be used in different therapeutic areas.
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Affiliation(s)
- Lucie Fayette
- Inserm, IAME, Université Paris Cité and Université Sorbonne Paris Nord, F-75018, Paris, France
- École des Ponts, UGE, Champs-sur-Marne, France
| | - Romain Leroux
- Inserm, IAME, Université Paris Cité and Université Sorbonne Paris Nord, F-75018, Paris, France
| | - France Mentré
- Inserm, IAME, Université Paris Cité and Université Sorbonne Paris Nord, F-75018, Paris, France
| | - Jérémy Seurat
- Inserm, IAME, Université Paris Cité and Université Sorbonne Paris Nord, F-75018, Paris, France.
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Baaz M, Cardilin T, Lignet F, Zimmermann A, El Bawab S, Gabrielsson J, Jirstrand M. Model-based assessment of combination therapies - ranking of radiosensitizing agents in oncology. BMC Cancer 2023; 23:409. [PMID: 37149596 PMCID: PMC10164338 DOI: 10.1186/s12885-023-10899-y] [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: 09/05/2021] [Accepted: 04/27/2023] [Indexed: 05/08/2023] Open
Abstract
BACKGROUND To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data. METHODS We used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves. RESULTS The model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 [Formula: see text] of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication. CONCLUSIONS A simulation-based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE-curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process.
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Affiliation(s)
- Marcus Baaz
- Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Gothenburg, Sweden.
- Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden.
| | - Tim Cardilin
- Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Gothenburg, Sweden
| | - Floriane Lignet
- Translational Medicine, Quantitative Pharmacology, Merck Healthcare KGaA, Darmstadt, Germany
| | - Astrid Zimmermann
- Translation Innovation Platform Oncology, Merck Healthcare KGaA, Darmstadt, Germany
| | - Samer El Bawab
- Translational Medicine, Quantitative Pharmacology, Merck Healthcare KGaA, Darmstadt, Germany
- Present Address: Translational Medicine, Servier, Suresnes, France
| | | | - Mats Jirstrand
- Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Gothenburg, Sweden
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Lill D, Kümmel A, Mitov V, Kaschek D, Gobeau N, Schmidt H, Timmer J. Efficient simulation of clinical target response surfaces. CPT Pharmacometrics Syst Pharmacol 2022; 11:512-523. [PMID: 35199969 PMCID: PMC9007598 DOI: 10.1002/psp4.12779] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 01/18/2022] [Accepted: 02/14/2022] [Indexed: 11/08/2022] Open
Abstract
Simulation of combination therapies is challenging due to computational complexity. Either a simple model is used to simulate the response for many combinations of concentration to generate a response surface but parameter variability and uncertainty are neglected and the concentrations are constant—the link to the doses to be administered is difficult to make—or a population pharmacokinetic/pharmacodynamic model is used to predict the response to combination therapy in a clinical trial taking into account the time‐varying concentration profile, interindividual variability (IIV), and parameter uncertainty but simulations are limited to only a few selected doses. We devised new algorithms to efficiently search for the combination doses that achieve a predefined efficacy target while taking into account the IIV and parameter uncertainty. The result of this method is a response surface of confidence levels, indicating for all dose combinations the likelihood of reaching the specified efficacy target. We highlight the importance to simulate across a population rather than focus on an individual. Finally, we provide examples of potential applications, such as informing experimental design.
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Affiliation(s)
- Daniel Lill
- IntiQuan GmbH Basel Switzerland
- Institute of Physics University of Freiburg Freiburg Germany
| | | | | | | | | | | | - Jens Timmer
- Institute of Physics University of Freiburg Freiburg Germany
- Centre for Integrative Biological Signalling Studies (CIBSS) University of Freiburg Freiburg Germany
- Freiburg Center for Data Analysis and Modelling (FDM) University of Freiburg Freiburg Germany
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Flournoy N, May C, Tommasi C. The effects of adaptation on maximum likelihood inference for nonlinear models with normal errors. J Stat Plan Inference 2021. [DOI: 10.1016/j.jspi.2021.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Ryeznik Y, Sverdlov O, Svensson EM, Montepiedra G, Hooker AC, Wong WK. Pharmacometrics meets statistics-A synergy for modern drug development. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:1134-1149. [PMID: 34318621 PMCID: PMC8520751 DOI: 10.1002/psp4.12696] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 05/17/2021] [Accepted: 07/02/2021] [Indexed: 01/20/2023]
Abstract
Modern drug development problems are very complex and require integration of various scientific fields. Traditionally, statistical methods have been the primary tool for design and analysis of clinical trials. Increasingly, pharmacometric approaches using physiology-based drug and disease models are applied in this context. In this paper, we show that statistics and pharmacometrics have more in common than what keeps them apart, and collectively, the synergy from these two quantitative disciplines can provide greater advances in clinical research and development, resulting in novel and more effective medicines to patients with medical need.
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Affiliation(s)
- Yevgen Ryeznik
- BioPharma Early Biometrics and Statistical Innovation, Data Science & AI, R&D Biopharmaceuticals, AstraZeneca, Gothenburg, Sweden
| | - Oleksandr Sverdlov
- Early Development Analytics, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA
| | - Elin M Svensson
- Department of Pharmacy, Uppsala University, Uppsala, Sweden.,Department of Pharmacy, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Grace Montepiedra
- Center for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | | | - Weng Kee Wong
- Department of Biostatistics, University of California Los Angeles, Los Angeles, California, USA
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Gontijo AVL, Cavalieri AVG. Optimal control for colistin dosage selection. J Pharmacokinet Pharmacodyn 2021; 48:803-813. [PMID: 34156631 PMCID: PMC8217983 DOI: 10.1007/s10928-021-09769-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Accepted: 06/12/2021] [Indexed: 12/04/2022]
Abstract
Optimization of antibiotic administration helps minimizing cases of bacterial resistance. Dosages are often selected by trial and error using a pharmacokinetic (PK) model. However, this is limited to the range of tested dosages, restraining possible treatment choices, especially for the loading doses. Colistin is a last-resort antibiotic with a narrow therapeutic window; therefore, its administration should avoid subtherapeutic or toxic concentrations. This study formulates an optimal control problem for dosage selection of colistin based on a PK model, minimizing deviations of colistin concentration to a target value and allowing a specific dosage optimization for a given individual. An adjoint model was used to provide the sensitivity of concentration deviations to dose changes. A three-compartment PK model was adopted. The standard deviation between colistin plasma concentrations and a target set at 2 mg/L was minimized for some chosen treatments and sample patients. Significantly lower deviations from the target concentration are obtained for shorter administration intervals (e.g. every 8 h) compared to longer ones (e.g. every 24 h). For patients with normal or altered renal function, the optimal loading dose regimen should be divided into two or more administrations to attain the target concentration quickly, with a high first loading dose followed by much lower ones. This regimen is not easily obtained by trial and error, highlighting advantages of the method. The present method is a refined optimization of antibiotic dosage for the treatment of infections. Results for colistin suggest significant improvement in treatment avoiding subtherapeutic or toxic concentrations.
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Affiliation(s)
- Aline Vidal Lacerda Gontijo
- Department of Clinical and Toxicological Analysis, Federal University of Alfenas, Rua Gabriel Monteiro da Silva 700, Centro, Alfenas, MG, 37130-001, Brazil.
- Department of Pharmacy, Anhanguera Educacional, São José dos Campos, SP, Brazil.
| | - André V G Cavalieri
- Divisão de Engenharia Aeroespacial, Instituto Tecnológico de Aeronáutica, São José dos Campos, SP, Brazil
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Seurat J, Girard P, Goteti K, Mentré F. Comparison of Various Phase I Combination Therapy Designs in Oncology for Evaluation of Early Tumor Shrinkage Using Simulations. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2020; 9:686-694. [PMID: 33080100 PMCID: PMC7762808 DOI: 10.1002/psp4.12564] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 09/21/2020] [Indexed: 12/11/2022]
Abstract
There is still a lack of efficient designs for identifying the dose response in oncology combination therapies in early clinical trials. The concentration response relationship can be identified using the early tumor shrinkage time course, which has been shown to be a good early response marker of clinical efficacy. The performance of various designs using an exposure–tumor growth inhibition model was explored using simulations. Different combination effects of new drug M and cetuximab (reference therapy) were explored first assuming no effect of M on cetuximab (to investigate the type I error (α)), and subsequently assuming additivity or synergy between cetuximab and M. One‐arm, two‐arm, and four‐arm designs were evaluated. In the one‐arm design, 60 patients received cetuximab + M. In the two‐arm design, 30 patients received cetuximab and 30 received cetuximab + M. In the four‐arm design, in addition to cetuximab and cetuximab + M as standard doses, combination arms with lower doses of cetuximab were evaluated (15 patients/arm). Model‐based predictions or “simulated observations” of early tumor shrinkage at week 8 (ETS8) were compared between the different arms. With the same number of individuals, the one‐arm design showed better statistical power than other designs but led to strong inflation of α in case of misestimated reference for ETS8 value. The two‐arm design protected against this misestimation and, with the same total number of subjects, would provide higher statistical power than a four‐arm design. However, a four‐arm design would be helpful for exploring more doses of cetuximab in combination with M to better understand the interaction.
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Affiliation(s)
- Jérémy Seurat
- Université de Paris, INSERM, IAME, F-75006 Paris, France
| | - Pascal Girard
- Merck Institute for Pharmacometrics, Merck Serono S.A, Lausanne, Switzerland
| | | | - France Mentré
- Université de Paris, INSERM, IAME, F-75006 Paris, France
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Mathematical model and tool to explore shorter multi-drug therapy options for active pulmonary tuberculosis. PLoS Comput Biol 2020; 16:e1008107. [PMID: 32810158 PMCID: PMC7480878 DOI: 10.1371/journal.pcbi.1008107] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 09/09/2020] [Accepted: 06/30/2020] [Indexed: 12/20/2022] Open
Abstract
Standard treatment for active tuberculosis (TB) requires drug treatment with at least four drugs over six months. Shorter-duration therapy would mean less need for strict adherence, and reduced risk of bacterial resistance. A system pharmacology model of TB infection, and drug therapy was developed and used to simulate the outcome of different drug therapy scenarios. The model incorporated human immune response, granuloma lesions, multi-drug antimicrobial chemotherapy, and bacterial resistance. A dynamic population pharmacokinetic/pharmacodynamic (PK/PD) simulation model including rifampin, isoniazid, pyrazinamide, and ethambutol was developed and parameters aligned with previous experimental data. Population therapy outcomes for simulations were found to be generally consistent with summary results from previous clinical trials, for a range of drug dose and duration scenarios. An online tool developed from this model is released as open source software. The TB simulation tool could support analysis of new therapy options, novel drug types, and combinations, incorporating factors such as patient adherence behavior. A comprehensive in-silico model of pulmonary tuberculosis successfully predicted previous clinical trials and could simulate future therapeutics.
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Guo Y, Haddish-Berhane N, Xie H, Ouellet D. Optimization of clinical dosing schedule to manage neutropenia: learnings from semi-mechanistic modeling simulation approach. J Pharmacokinet Pharmacodyn 2019; 47:47-58. [PMID: 31853740 DOI: 10.1007/s10928-019-09667-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 12/03/2019] [Indexed: 11/27/2022]
Abstract
Neutropenia is a common side-effect of oncology drugs. We aimed to study the impact of exposure and dosing schedule on neutropenia to guide selection of dosing schedules that minimize neutropenia potential while maintaining the desired minimum concentration (Cmin) required for target engagement. Dose, frequency and PK parameters were chosen for five hypothetical drugs of various half-lives to (1) achieve same exposure with continuous dosing and evaluate impact of 4 intermittent dosing schedules; and (2) achieve same nadir for continuous and intermittent dosing and evaluate impact on % time above Cmin, a surrogate assumed to indicate target engagement. Absolute neutrophil count (ANC) profiles were simulated using Friberg model, a widely used semi-mechanistic myelosuppression model, assuming drug concentration directly reduce the proliferation rate of stem cells and progenitor cells in proliferation compartment. The correlations between different PK measures and neutropenia metrics were explored. In (1), when the same daily dose was used, intermittent schedules offered better management of ANC nadir. The reduced average drug exposure with intermittent dosing led to lower% time above Cmin. In (2), when the dose was adjusted to achieve the same nadir, drugs with moderate half-life (8-48 h) showed similar % time above Cmin regardless of schedule, while continuous dosing was better for a short half-life (4 h). Area under the concentration curve (AUC) was highly correlated with neutropenia. In summary, continuous dosing, with the dose selected correctly, is most effective to maintain % time above Cmin while providing similar tolerability as intermittent dosing with a higher dose. But dose interruptions could be required to manage individual toxicities. Intermittent schedules, on the other hand, allow recovery of ANC, enabling more orderly schedules.
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Affiliation(s)
- Yue Guo
- Clinical Pharmacology and Pharmacometrics, Quantitative Sciences, Janssen Research & Development, Spring House, PA, USA.
| | - Nahor Haddish-Berhane
- Clinical Pharmacology and Pharmacometrics, Quantitative Sciences, Janssen Research & Development, Spring House, PA, USA
| | - Hong Xie
- Oncology Early Development, Janssen Research & Development, 1400 McKean Rd, Spring House, PA, 19002, USA
| | - Daniele Ouellet
- Clinical Pharmacology and Pharmacometrics, Quantitative Sciences, Janssen Research & Development, Spring House, PA, USA
- Pfizer Research and Development, Collegeville, PA, USA
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Nøhr‐Nielsen A, De Bruin ML, Thomsen M, Pipper CB, Lange T, Bjerrum OJ, Lund TM. Body of evidence and approaches applied in the clinical development programme of fixed-dose combinations in the European Union from 2010 to 2016. Br J Clin Pharmacol 2019; 85:1829-1840. [PMID: 31077427 PMCID: PMC6624404 DOI: 10.1111/bcp.13986] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Revised: 04/16/2019] [Accepted: 04/29/2019] [Indexed: 12/21/2022] Open
Abstract
AIMS To provide insights into the clinical development pathway for fixed-dose combinations (FDCs), to consider strategies, and to elucidate the path to approval by assessing the body of evidence, as summarized in the European Public Assessment Reports. METHODS The main resource was the European Public Assessment Reports for 36 FDCs, which included 239 clinical trials with 157 514 patients. The analyses focused on how prior knowledge of the active substances or combination, use of pharmacokinetic-pharmacodynamic modelling, and clinical trial design choice impact the size and strategy of the clinical development programme. RESULTS FDC products primarily comprised 2 previously approved components (21/36, 71%) and had only 1 approved combination (21/36, 71%). Utilizing previously approved active substances resulted in fewer clinical trials, arms and patients, but FDC doses studied in the clinical development programme. Furthermore, dose-finding trials were performed for less than half of FDCs consisting of 2 previously approved active substances. The standard approach to demonstrate contribution of active substances was through a factorial or single combination study. Finally, the use of pharmacokinetic modelling showed a significant decrease in the number of FDC doses studied. CONCLUSIONS The field of FDCs seems to be on the rise, utilizing new molecular entities, prior knowledge and re-profiling drugs. However, a way to move FDC development forward might be through new regulatory and scientific paradigms, in which it is encouraged to utilize model-based approaches to develop FDCs with multiple dose levels and dose ratios for exposure-based treatment that will enable personalization.
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Affiliation(s)
- Asbjørn Nøhr‐Nielsen
- Department of Drug Design and Pharmacology, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
- Copenhagen Centre for Regulatory ScienceUniversity of CopenhagenCopenhagenDenmark
| | | | | | | | - Theis Lange
- Department of Public HealthUniversity of CopenhagenCopenhagenDenmark
| | - Ole Jannik Bjerrum
- Department of Drug Design and Pharmacology, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
| | - Trine Meldgaard Lund
- Department of Drug Design and Pharmacology, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
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Papathanasiou T, Strathe A, Overgaard RV, Lund TM, Hooker AC. Optimizing Dose-Finding Studies for Drug Combinations Based on Exposure-Response Models. AAPS JOURNAL 2019; 21:95. [DOI: 10.1208/s12248-019-0365-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 07/09/2019] [Indexed: 12/30/2022]
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Bottino DC, Patel M, Kadakia E, Zhou J, Patel C, Neuwirth R, Iartchouk N, Brake R, Venkatakrishnan K, Chakravarty A. Dose Optimization for Anticancer Drug Combinations: Maximizing Therapeutic Index via Clinical Exposure-Toxicity/Preclinical Exposure-Efficacy Modeling. Clin Cancer Res 2019; 25:6633-6643. [PMID: 31320596 DOI: 10.1158/1078-0432.ccr-18-3882] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 05/31/2019] [Accepted: 07/11/2019] [Indexed: 11/16/2022]
Abstract
PURPOSE Recommended phase II dose (RP2D) determination for combination therapy regimens is a constrained optimization problem of maximizing antitumor activity within the constraint of clinical tolerability to provide a wide therapeutic index. A methodology for addressing this problem was developed and tested using clinical and preclinical data from combinations of the investigational drugs TAK-117, a PI3Kα inhibitor, and TAK-228, a TORC1/2 dual inhibitor. EXPERIMENTAL DESIGN Utilizing free fraction-corrected average concentrations, [Formula: see text] and [Formula: see text], which are the primary pharmacokinetic predictors of single-agent preclinical antitumor activity, a preclinical exposure-efficacy surface was characterized, allowing for nonlinear interactions between growth rate inhibition of the agents on a MDA-MB-361 cell line xenograft model. Logistic regression was used to generate an exposure-effect surface for [Formula: see text] and [Formula: see text] versus clinical toxicity outcomes [experiencing a dose-limiting toxicity (DLT)] in single-agent and combination dose-escalation studies. A maximum tolerated exposure curve was defined at which DLT probability was 25%; predicted antitumor activity along this curve was used to determine optimal RP2D. RESULTS The toxicity constraint curve determined from early clinical data predicted that any clinically tolerable combination was unlikely to result in greater antitumor activity than either single-agent TAK-117 or TAK-228 administered at their respective MTDs. Similar results were obtained with 10 other cell lines, with one agent or the other predicted to outperform the combination. CONCLUSIONS This methodology represents a general, principled way of evaluating and selecting optimal RP2D combinations in oncology. The methodology will be retested upon availability of clinical data from TAK-117/TAK-228 combination phase II studies.See related commentary by Mayawala et al., p. 6564.
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Affiliation(s)
- Dean C Bottino
- Quantitative Clinical Pharmacology, Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts.
| | - Mayankbhai Patel
- DMPK Modeling & Simulation, Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts
| | - Ekta Kadakia
- DMPK Modeling & Simulation, Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts
| | - Jilai Zhou
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire
| | - Chirag Patel
- Quantitative Clinical Pharmacology, Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts
| | - Rachel Neuwirth
- Biostatistics, Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts
| | - Natasha Iartchouk
- Oncology Drug Development Unit, Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts
| | - Rachael Brake
- Oncology Therapeutic Area Unit, Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts
| | - Karthik Venkatakrishnan
- Quantitative Clinical Pharmacology, Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts
| | - Arijit Chakravarty
- DMPK Modeling & Simulation, Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts
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