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Rodríguez-Mauriz R, González-Laguna M, Perayre-Badia M, Lozano-Andreu T, Miquel-Zurita ME, Cañizares-Paz S, Santulario-Verdú L, Millan-Coll M, Fontanals S, Clopés-Estela A. Pharmaceutical care in the screening process of phase I oncohaematological clinical trials. Eur J Hosp Pharm 2024:ejhpharm-2024-004168. [PMID: 39137972 DOI: 10.1136/ejhpharm-2024-004168] [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: 03/11/2024] [Accepted: 07/29/2024] [Indexed: 08/15/2024] Open
Abstract
OBJECTIVE To determine the pharmaceutical interventions in patients eligible for phase I cancer clinical trials, focusing specifically on exclusion criteria related to medication or relevant interactions. METHOD Descriptive, observational study conducted at a comprehensive cancer centre. Patients undergoing screening for phase I clinical trials (March 2019-December 2022) were included. The pharmacist reviewed concomitant medication and provided a recommendation. RESULTS The concomitant medication of 512 patients eligible to participate in 84 phase I clinical trials was analysed. In 230 (44.9%) patients, the clinical trial treatment included oral medication. The median number of concomitant medications was 5 (IQR 3-8) per patient.A total of 280 pharmaceutical interventions were performed in 140 (27.3%) patients: 240 (85.7%) were due to interactions in 124 (24.2%) patients, and 40 (14.3%) were due to exclusion criteria in 34 (6.6%) patients. Interactions and exclusion criteria were detected in 18 (3.5%) patients. The main groups of drugs involved were 68 (24.3%) antacids and antiulcer drugs, 28 (10.0%) antidepressants and 26 (9.3%) opioids. Acceptance analysis of the recommendation was applicable in 215 cases; in 208 (96.7%), the pharmaceutical intervention was accepted.Differences were identified for exclusion criteria (7 vs 27) and interactions (37 vs 87) between parenteral and oral clinical trial medication (p<0.001). CONCLUSION The pharmacist's review of concomitant medication during the screening period in phase I clinical trials enables the detection of prohibited medication or relevant interactions, potentially avoiding screening failures and increasing the efficacy and safety of treatments.
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Affiliation(s)
- Rosa Rodríguez-Mauriz
- Pharmacy Department, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Monica González-Laguna
- Pharmacy Department, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Maria Perayre-Badia
- Pharmacy Department, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Toni Lozano-Andreu
- Pharmacy Department, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
| | | | - Salomé Cañizares-Paz
- Pharmacy Department, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Lorena Santulario-Verdú
- Pharmacy Department, Bellvitge University Hospital, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Marina Millan-Coll
- Pharmacy Department, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Sandra Fontanals
- Pharmacy Department, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Instituto de Investigación Biomédica de Bellvitge, Barcelona, Spain
| | - Ana Clopés-Estela
- Pharmacy Department, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Instituto de Investigación Biomédica de Bellvitge, Barcelona, Spain
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2
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Pantoja K, Lanke S, Munafo A, Victor A, Habermehl C, Schueler A, Venkatakrishnan K, Girard P, Goteti K. Designing phase I oncology dose escalation using dose-exposure-toxicity models as a complementary approach to model-based dose-toxicity models. CPT Pharmacometrics Syst Pharmacol 2022; 11:1371-1381. [PMID: 35852048 PMCID: PMC9574748 DOI: 10.1002/psp4.12851] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/05/2022] [Accepted: 07/11/2022] [Indexed: 11/29/2022] Open
Abstract
One of the objectives of oncology phase I dose-escalation studies has been to determine the maximum tolerated dose (MTD). Although MTD is no longer set as the dose for further development in contemporary oncology drug development, MTD determination is still important for informing the therapeutic index. Bayesian adaptive model-based designs are becoming mainstream in oncology first-in-human trials. Herein, we illustrate via simulations the use of systemic exposure in Bayesian adaptive dose-toxicity models to estimate MTD. We extend traditional dose-toxicity models to incorporate pharmacokinetic exposure, which provides information on exposure-toxicity relationships. We pursue dose escalation until the maximum tolerated exposure (corresponding to the MTD) is reached. By leveraging pharmacokinetics, dose escalation considers exposure and interindividual variability on a continuous rather than discrete domain, offering additional information for dose-escalation decisions. To demonstrate this, we generated 1000 simulations (starting dose of 1/25th the reference dose and six dose levels) for several different scenarios. Both rule-based and model-based designs were compared using metrics of potential safety, accuracy, and reliability. The mean results over simulations and different toxicity scenarios showed that model-based designs were better than rule-based methods and that exposure-toxicity model-based methods have the potential to valuably complement dose-toxicity model-based methods. Exposure-toxicity model-based methods had decreased underdose risk accompanied by a relatively smaller increase in overdose risk, resulting in improved net reliability. MTD estimation accuracy was compromised when exposure variability was large, emphasizing the importance of appropriate control of pharmacokinetic variability in phase I dose-escalation studies.
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Affiliation(s)
- Kristyn Pantoja
- Department of StatisticsTexas A&M UniversityCollege StationTexasUSA
- EMD Serono Research InstituteBillericaMassachusettsUSA
| | - Shankar Lanke
- EMD Serono Research InstituteBillericaMassachusettsUSA
| | - Alain Munafo
- Merck Institute for PharmacometricsLausanneSwitzerland
| | | | | | | | | | - Pascal Girard
- Merck Institute for PharmacometricsLausanneSwitzerland
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3
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Hariparsad N, Ramsden D, Taskar K, Badée J, Venkatakrishnan K, Reddy MB, Cabalu T, Mukherjee D, Rehmel J, Bolleddula J, Emami Riedmaier A, Prakash C, Chanteux H, Mao J, Umehara K, Shah K, De Zwart L, Dowty M, Kotsuma M, Li M, Pilla Reddy V, McGinnity DF, Parrott N. Current Practices, Gap Analysis, and Proposed Workflows for PBPK Modeling of Cytochrome P450 Induction: An Industry Perspective. Clin Pharmacol Ther 2021; 112:770-781. [PMID: 34862964 DOI: 10.1002/cpt.2503] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 11/18/2021] [Indexed: 12/21/2022]
Abstract
The International Consortium for Innovation and Quality (IQ) Physiologically Based Pharmacokinetic (PBPK) Modeling Induction Working Group (IWG) conducted a survey across participating companies around general strategies for PBPK modeling of induction, including experience with its utility to address various questions, regulatory interactions, and regulatory acceptance. The results highlight areas where PBPK modeling is used with high confidence and identifies opportunities where confidence is lower and further evaluation is needed. To enhance the survey results, the PBPK-IWG also collected case studies and analyzed recent literature examples where PBPK models were applied to predict CYP3A induction-mediated drug-drug interactions. PBPK modeling of induction has evolved and progressed significantly, proving to have great potential to accelerate drug discovery and development. With the aim of enabling optimal use for new molecular entities that are either substrates and/or inducers of CYP3A, the PBPK-IWG proposes initial workflows for PBPK application, discusses future trends, and identifies gaps that need to be addressed.
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Affiliation(s)
- Niresh Hariparsad
- DMPK, Research and Early Development, Oncology R&D, AstraZeneca, Boston, Massachusetts, USA
| | - Diane Ramsden
- Takeda Development Center Americas, Inc., Cambridge, Massachusetts, USA
| | - Kunal Taskar
- Drug Metabolism and Pharmacokinetics, IVIVT, GlaxoSmithKline, Stevenage, UK
| | - Justine Badée
- PK Sciences, Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Karthik Venkatakrishnan
- EMD Serono Research & Development Institute, Inc, Billerica, Massachusetts, USA.,Merck KGaA, Darmstadt, Germany
| | - Micaela B Reddy
- Department of Clinical Pharmacology, Oncology, Pfizer, Boulder, Colorado, USA
| | | | - Dwaipayan Mukherjee
- Clinical Pharmacology & Pharmacometrics, AbbVie, Inc., North Chicago, Illinois, USA
| | - Jessica Rehmel
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, USA
| | - Jayaprakasam Bolleddula
- EMD Serono Research & Development Institute, Inc, Billerica, Massachusetts, USA.,Merck KGaA, Darmstadt, Germany
| | | | | | | | - Jialin Mao
- Department of Drug Metabolism and Pharmacokinetics, Genentech, A Member of the Roche Group, South San Francisco, California, USA
| | - Kenichi Umehara
- Pharmaceutical Sciences, Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Kushal Shah
- Drug Metabolism and Pharmacokinetics, Vertex Pharmaceuticals Incorporated, Boston, Massachusetts, USA
| | | | - Martin Dowty
- Department of Pharmacokinetics, Dynamic, and Metabolism, Pfizer, Cambridge, Massachusetts, USA
| | - Masakatsu Kotsuma
- Quantitative Clinical Pharmacology, Daiichi-Sankyo, Inc., New Jersey, USA
| | - Mengyao Li
- Pharmacokinetics, Dynamics and Metabolism, Sanofi, Bridgewater, New Jersey, USA
| | - Venkatesh Pilla Reddy
- Clinical Pharmacology and Pharmacometrics, Biopharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Dermot F McGinnity
- DMPK, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Neil Parrott
- Pharmaceutical Sciences, Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland
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4
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Darwich AS, von Moltke L. The Impact of Formulation, Delivery, and Dosing Regimen on the Risk of Drug-Drug Interactions. Clin Pharmacol Ther 2019; 105:1329-1331. [PMID: 30897206 DOI: 10.1002/cpt.1395] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 02/03/2019] [Indexed: 11/12/2022]
Affiliation(s)
- Adam S Darwich
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, The University of Manchester, Manchester, UK
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5
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Zhou X, Pant S, Nemunaitis J, Craig Lockhart A, Falchook G, Bauer TM, Patel M, Sarantopoulos J, Bargfrede M, Muehler A, Rangachari L, Zhang B, Venkatakrishnan K. Effects of rifampin, itraconazole and esomeprazole on the pharmacokinetics of alisertib, an investigational aurora a kinase inhibitor in patients with advanced malignancies. Invest New Drugs 2017; 36:248-258. [PMID: 28852909 PMCID: PMC5869871 DOI: 10.1007/s10637-017-0499-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Accepted: 08/10/2017] [Indexed: 11/27/2022]
Abstract
Aim Two studies investigated the effect of gastric acid reducing agents and strong inducers/inhibitors of CYP3A4 on the pharmacokinetics of alisertib, an investigational Aurora A kinase inhibitor, in patients with advanced malignancies. Methods In Study 1, patients received single doses of alisertib (50 mg) in the presence and absence of either esomeprazole (40 mg once daily [QD]) or rifampin (600 mg QD). In Study 2, patients received single doses of alisertib (30 mg) in the presence and absence of itraconazole (200 mg QD). Blood samples for alisertib and 2 major metabolites were collected up to 72 h (Study 1) and 96 h (Study 2) postdose. Area under the curve from time zero extrapolated to infinity (AUC0-inf) and maximum concentrations (Cmax) were calculated and compared using analysis of variance to estimate least squares (LS) mean ratios and 90% confidence intervals (CIs). Results The LS mean ratios (90% CIs) for alisertib AUC0-inf and Cmax in the presence compared to the absence of esomeprazole were 1.28 (1.07, 1.53) and 1.14 (0.97, 1.35), respectively. The LS mean ratios (90% CIs) for alisertib AUC0-inf and Cmax in the presence compared to the absence of rifampin were 0.53 (0.41, 0.70) and 1.03 (0.84, 1.26), respectively. The LS mean ratios (90% CIs) for alisertib AUC0-inf and Cmax in the presence compared to the absence of itraconazole were 1.39 (0.99, 1.95) and 0.98 (0.82, 1.19), respectively. Conclusions The use of gastric acid reducing agents, strong CYP3A inhibitors or strong metabolic enzyme inducers should be avoided in patients receiving alisertib.
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Affiliation(s)
- Xiaofei Zhou
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, MA, USA.
| | - Shubham Pant
- Oklahoma University Medical Center, Oklahoma City, OK, USA
| | | | | | - Gerald Falchook
- Sarah Cannon Research Institute at HealthONE, Denver, CO, USA
| | - Todd M Bauer
- Sarah Cannon Research Institute, Nashville, TN, USA
| | | | - John Sarantopoulos
- Institute for Drug Development, Cancer Therapy and Research Center, University of Texas Health Science Center San Antonio, San Antonio, TX, USA
| | | | - Andreas Muehler
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, MA, USA
| | - Lakshmi Rangachari
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, MA, USA
| | - Bin Zhang
- Seqirus Pharmaceuticals, Cambridge, MA, USA
| | - Karthik Venkatakrishnan
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, MA, USA
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6
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Gupta N, Hanley MJ, Venkatakrishnan K, Bessudo A, Rasco DW, Sharma S, O'Neil BH, Wang B, Liu G, Ke A, Patel C, Rowland Yeo K, Xia C, Zhang X, Esseltine DL, Nemunaitis J. Effects of Strong CYP3A Inhibition and Induction on the Pharmacokinetics of Ixazomib, an Oral Proteasome Inhibitor: Results of Drug-Drug Interaction Studies in Patients With Advanced Solid Tumors or Lymphoma and a Physiologically Based Pharmacokinetic Analysis. J Clin Pharmacol 2017; 58:180-192. [PMID: 28800141 PMCID: PMC5811830 DOI: 10.1002/jcph.988] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Accepted: 06/28/2017] [Indexed: 12/27/2022]
Abstract
At clinically relevant ixazomib concentrations, in vitro studies demonstrated that no specific cytochrome P450 (CYP) enzyme predominantly contributes to ixazomib metabolism. However, at higher than clinical concentrations, ixazomib was metabolized by multiple CYP isoforms, with the estimated relative contribution being highest for CYP3A at 42%. This multiarm phase 1 study (Clinicaltrials.gov identifier: NCT01454076) investigated the effect of the strong CYP3A inhibitors ketoconazole and clarithromycin and the strong CYP3A inducer rifampin on the pharmacokinetics of ixazomib. Eighty-eight patients were enrolled across the 3 drug-drug interaction studies; the ixazomib toxicity profile was consistent with previous studies. Ketoconazole and clarithromycin had no clinically meaningful effects on the pharmacokinetics of ixazomib. The geometric least-squares mean area under the plasma concentration-time curve from 0 to 264 hours postdose ratio (90%CI) with vs without ketoconazole coadministration was 1.09 (0.91-1.31) and was 1.11 (0.86-1.43) with vs without clarithromycin coadministration. Reduced plasma exposures of ixazomib were observed following coadministration with rifampin. Ixazomib area under the plasma concentration-time curve from time 0 to the time of the last quantifiable concentration was reduced by 74% (geometric least-squares mean ratio of 0.26 [90%CI 0.18-0.37]), and maximum observed plasma concentration was reduced by 54% (geometric least-squares mean ratio of 0.46 [90%CI 0.29-0.73]) in the presence of rifampin. The clinical drug-drug interaction study results were reconciled well by a physiologically based pharmacokinetic model that incorporated a minor contribution of CYP3A to overall ixazomib clearance and quantitatively considered the strength of induction of CYP3A and intestinal P-glycoprotein by rifampin. On the basis of these study results, the ixazomib prescribing information recommends that patients should avoid concomitant administration of strong CYP3A inducers with ixazomib.
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Affiliation(s)
- Neeraj Gupta
- Millennium Pharmaceuticals, Inc, Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited
| | - Michael J Hanley
- Millennium Pharmaceuticals, Inc, Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited
| | - Karthik Venkatakrishnan
- Millennium Pharmaceuticals, Inc, Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited
| | - Alberto Bessudo
- California Cancer Associates for Research and Excellence, San Diego, CA, USA
| | - Drew W Rasco
- South Texas Accelerated Research Therapeutics, San Antonio, TX, USA
| | - Sunil Sharma
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Bert H O'Neil
- Indiana University Simon Cancer Center, Indianapolis, IN, USA
| | - Bingxia Wang
- Millennium Pharmaceuticals, Inc, Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited
| | - Guohui Liu
- Millennium Pharmaceuticals, Inc, Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited
| | - Alice Ke
- Certara USA, Inc, Princeton, NJ, USA
| | - Chirag Patel
- Millennium Pharmaceuticals, Inc, Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited
| | | | - Cindy Xia
- Millennium Pharmaceuticals, Inc, Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited
| | - Xiaoquan Zhang
- Millennium Pharmaceuticals, Inc, Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited
| | - Dixie-Lee Esseltine
- Millennium Pharmaceuticals, Inc, Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited
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7
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Venkatakrishnan K, Burgess C, Gupta N, Suri A, Takubo T, Zhou X, DeMuria D, Lehnert M, Takeyama K, Singhvi S, Milton A. Toward Optimum Benefit-Risk and Reduced Access Lag For Cancer Drugs in Asia: A Global Development Framework Guided by Clinical Pharmacology Principles. Clin Transl Sci 2016; 9:9-22. [PMID: 26836226 PMCID: PMC5351319 DOI: 10.1111/cts.12386] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Revised: 12/22/2015] [Accepted: 01/05/2016] [Indexed: 12/13/2022] Open
Affiliation(s)
- K Venkatakrishnan
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited), Cambridge, Massachusetts, USA
| | - C Burgess
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited), Cambridge, Massachusetts, USA
| | - N Gupta
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited), Cambridge, Massachusetts, USA
| | - A Suri
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited), Cambridge, Massachusetts, USA
| | - T Takubo
- Takeda Pharmaceutical Company Limited, Tokyo, Japan
| | - X Zhou
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited), Cambridge, Massachusetts, USA
| | - D DeMuria
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited), Cambridge, Massachusetts, USA
| | - M Lehnert
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited), Cambridge, Massachusetts, USA
| | - K Takeyama
- Takeda Pharmaceutical Company Limited, Tokyo, Japan
| | - S Singhvi
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited), Cambridge, Massachusetts, USA
| | - A Milton
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited), Cambridge, Massachusetts, USA
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8
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Venkatakrishnan K, Friberg LE, Ouellet D, Mettetal JT, Stein A, Trocóniz IF, Bruno R, Mehrotra N, Gobburu J, Mould DR. Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities. Clin Pharmacol Ther 2014; 97:37-54. [PMID: 25670382 DOI: 10.1002/cpt.7] [Citation(s) in RCA: 76] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2014] [Accepted: 10/15/2014] [Indexed: 01/01/2023]
Abstract
Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety.
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Affiliation(s)
- K Venkatakrishnan
- Clinical Pharmacology, Takeda Pharmaceuticals International Co., Cambridge, Massachusetts, USA
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9
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Tang J, Aittokallio T. Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles. Curr Pharm Des 2014; 20:23-36. [PMID: 23530504 PMCID: PMC3894695 DOI: 10.2174/13816128113199990470] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2013] [Accepted: 03/18/2013] [Indexed: 12/12/2022]
Abstract
Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However, to really capitalize on the polypharmacological effects of drugs, there is a critical need to better model and understand how the complex interactions between drugs and their cellular targets contribute to drug efficacy and possible side effects. Network graphs provide a convenient modeling framework for dealing with the fact that most drugs act on cellular systems through targeting multiple proteins both through on-target and off-target binding. Network pharmacology models aim at addressing questions such as how and where in the disease network should one target to inhibit disease phenotypes, such as cancer growth, ideally leading to therapies that are less vulnerable to drug resistance and side effects by means of attacking the disease network at the systems level through synergistic and synthetic lethal interactions. Since the exponentially increasing number of potential drug target combinations makes pure experimental approach quickly unfeasible, this review depicts a number of computational models and algorithms that can effectively reduce the search space for determining the most promising combinations for experimental evaluation. Such computational-experimental strategies are geared toward realizing the full potential of multi-target treatments in different disease phenotypes. Our specific focus is on system-level network approaches to polypharmacology designs in anticancer drug discovery, where we give representative examples of how network-centric modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies.
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10
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Bloomer J, Derimanov G, Dumont E, Ellens H, Matheny C. Optimizing thein vitroand clinical assessment of drug interaction risk by understanding co-medications in patient populations. Expert Opin Drug Metab Toxicol 2013; 9:737-51. [DOI: 10.1517/17425255.2013.781582] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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11
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Drug–Drug Interaction Potential of Marketed Oncology Drugs: In Vitro Assessment of Time-Dependent Cytochrome P450 Inhibition, Reactive Metabolite Formation and Drug–Drug Interaction Prediction. Pharm Res 2012; 29:1960-76. [DOI: 10.1007/s11095-012-0724-6] [Citation(s) in RCA: 72] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2011] [Accepted: 02/27/2012] [Indexed: 12/11/2022]
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12
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Journal Watch. Pharmaceut Med 2010. [DOI: 10.1007/bf03256839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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