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A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development. J Pharmacokinet Pharmacodyn 2023; 50:147-172. [PMID: 36870005 PMCID: PMC10169901 DOI: 10.1007/s10928-023-09850-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 02/16/2023] [Indexed: 03/05/2023]
Abstract
Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.
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2
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Guo Y, Yong L, Yao Q, Han M, Xue J, Jian W, Zhou T. Application of a count data model to evaluate the anti-metastatic efficacy of QAP14 in 4T1 breast cancer allografts. J Theor Biol 2023; 557:111323. [PMID: 36273592 DOI: 10.1016/j.jtbi.2022.111323] [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: 01/05/2022] [Revised: 09/01/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
Abstract
Dopamine D1 receptor (D1DR) is proved to be a promising target to prevent tumor metastasis, and our previous studies showed that QAP14, a potent anti-cancer agent, exerted inhibitory effect on lung metastasis via D1DR activation. Therefore, the purpose of the study was to establish count data models to quantitatively characterize the disease progression of lung metastasis and assess the anti-metastatic effect of QAP14. Data of metastatic progression were collected in 4T1 tumor-bearing mice. Generalized Poisson distribution best described the variability of metastasis counts among the individuals. An empirical PK/PD model was developed to establish mathematical relationships between steady plasma concentrations of QAP14 and metastasis growth dynamics. The latency period of metastasis was estimated to be 12 days after tumor implantation. Our model structure also fitted well to other D1DR agonists (fenoldopam and l-stepholidine) which had inhibitory impact on breast cancer lung metastasis likewise. QAP14 40 mg/kg showed the best inhibitory efficacy, for it provided the longest prolongation of metastasis-free periods compared with fenoldopam or l-stepholidine. This study provides a quantitative method to describe the lung metastasis progression of 4T1 allografts, as well as an alternative PD model structure to evaluate anti-metastatic efficacy.
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Affiliation(s)
- Yuchen Guo
- Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System, Department of Pharmaceutics, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Ling Yong
- Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System, Department of Pharmaceutics, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Qingyu Yao
- Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System, Department of Pharmaceutics, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Mengyi Han
- Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System, Department of Pharmaceutics, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Junsheng Xue
- Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System, Department of Pharmaceutics, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Weizhe Jian
- Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System, Department of Pharmaceutics, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Tianyan Zhou
- Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System, Department of Pharmaceutics, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China.
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3
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Sung HW, Choi SE, Chu CH, Ouyang M, Kalyan S, Scott N, Hur SC. Sensitizing drug-resistant cancer cells from blood using microfluidic electroporator. PLoS One 2022; 17:e0264907. [PMID: 35259174 PMCID: PMC8903260 DOI: 10.1371/journal.pone.0264907] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/21/2022] [Indexed: 02/06/2023] Open
Abstract
Direct assessment of patient samples holds unprecedented potential in the treatment of cancer. Circulating tumor cells (CTCs) in liquid biopsies are a rapidly evolving source of primary cells in the clinic and are ideal candidates for functional assays to uncover real-time tumor information in real-time. However, a lack of routines allowing direct and active interrogation of CTCs directly from liquid biopsy samples represents a bottleneck for the translational use of liquid biopsies in clinical settings. To address this, we present a workflow for using a microfluidic vortex-assisted electroporation system designed for the functional assessment of CTCs purified from blood. Validation of this approach was assessed through drug response assays on wild-type (HCC827 wt) and gefitinib-resistant (HCC827 GR6) non-small cell lung cancer (NSCLC) cells. HCC827 cells trapped within microscale vortices were electroporated to sequentially deliver drug agents into the cytosol. Electroporation conditions facilitating multi-agent delivery were characterized for both cell lines using an automatic single-cell image fluorescence intensity algorithm. HCC827 GR6 cells spiked into the blood to emulate drug-resistant CTCs were able to be collected with high purity, demonstrating the ability of the device to minimize background cell impact for downstream sensitive cell assays. Using our proposed workflow, drug agent combinations to restore gefitinib sensitivity reflected the anticipated cytotoxic response. Taken together, these results represent a microfluidics multi-drug screening panel workflow that can enable functional interrogation of patient CTCs in situ, thereby accelerating the clinical standardization of liquid biopsies.
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Affiliation(s)
- Hyun Woo Sung
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Sung-Eun Choi
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Chris H. Chu
- Department of Internal Medicine, Virginia Mason Medical Center, Seattle, Washington, United States of America
| | - Mengxing Ouyang
- Department of Bioengineering, University of California, Los Angeles, California, United States of America
| | - Srivathsan Kalyan
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Nathan Scott
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Soojung Claire Hur
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Hospital, Baltimore, Maryland, United States of America
- Institute of NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland, United States of America
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4
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Netterberg I, Karlsson MO, Terstappen LWMM, Koopman M, Punt CJA, Friberg LE. Comparing Circulating Tumor Cell Counts with Dynamic Tumor Size Changes as Predictor of Overall Survival: A Quantitative Modeling Framework. Clin Cancer Res 2020; 26:4892-4900. [PMID: 32527941 DOI: 10.1158/1078-0432.ccr-19-2570] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 01/04/2020] [Accepted: 06/04/2020] [Indexed: 11/16/2022]
Abstract
PURPOSE Quantitative relationships between treatment-induced changes in tumor size and circulating tumor cell (CTC) counts, and their links to overall survival (OS), are lacking. We present a population modeling framework identifying and quantifying such relationships, based on longitudinal data collected in patients with metastatic colorectal cancer (mCRC) to evaluate the value of tumor size and CTC counts as predictors of OS. EXPERIMENTAL DESIGN A pharmacometric approach (i.e., population pharmacodynamic modeling) was used to characterize the changes in tumor size and CTC count and evaluate them as predictors of OS in 451 patients with mCRC treated with chemotherapy and targeted therapy in a prospectively randomized phase III study (CAIRO2). RESULTS A tumor size model of tumor quiescence and drug resistance was used to characterize the tumor size time-course, and was, in addition to the total normalized dose (i.e., of all administered drugs) in a given cycle, related to the CTC counts through a negative binomial model (CTC model). Tumor size changes did not contribute additional predictive value when the mean CTC count was a predictor of OS. Treatment reduced the typical mean count from 1.43 to 0.477 (HR = 3.94). The modeling framework was applied to explore whether dose modifications (increased and reduced) would result in a CTC count below 1/7.5 mL after 1 to 2 weeks of treatment. CONCLUSIONS Time-varying CTC counts can be useful for early predicting OS in patients with mCRC, and may therefore have potential for model-based treatment individualization. Although tumor size was connected to CTC, its link to OS was weaker.
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Affiliation(s)
- Ida Netterberg
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Leon W M M Terstappen
- Department of Medical Cell BioPhysics, Faculty of Science and Technology, University of Twente, Enschede, the Netherlands
| | - Miriam Koopman
- Department of Medical Oncology, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Cornelis J A Punt
- Department of Medical Oncology, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, the Netherlands
| | - Lena E Friberg
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
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5
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Sarathi Kannan D, Mahboob S, Al-Ghanim KA, Venkatachalam P. Antibacterial, Antibiofilm and Photocatalytic Activities of Biogenic Silver Nanoparticles from Ludwigia octovalvis. J CLUST SCI 2020. [DOI: 10.1007/s10876-020-01784-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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6
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Riglet F, Mentre F, Veyrat-Follet C, Bertrand J. Bayesian Individual Dynamic Predictions with Uncertainty of Longitudinal Biomarkers and Risks of Survival Events in a Joint Modelling Framework: a Comparison Between Stan, Monolix, and NONMEM. AAPS JOURNAL 2020; 22:50. [PMID: 32076894 DOI: 10.1208/s12248-019-0388-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Accepted: 10/30/2019] [Indexed: 12/14/2022]
Abstract
Given a joint model and its parameters, Bayesian individual dynamic prediction (IDP) of biomarkers and risk of event can be performed for new patients at different landmark times using observed biomarker values. The aim of the present study was to compare IDP, with uncertainty, using Stan 2.18, Monolix 2018R2 and NONMEM 7.4. Simulations of biomarker and survival were performed using a nonlinear joint model of prostate-specific antigen (PSA) kinetics and survival in metastatic prostate cancer. Several scenarios were evaluated, according to the strength of the association between PSA and survival. For various landmark times, a posteriori distribution of PSA kinetic individual parameters was estimated, given individual observations, with each software. Samples of individual parameters were drawn from the posterior distribution. Bias and imprecision of individual parameters as well as coverage of 95% credibility interval for PSA and risk of death were evaluated. All software performed equally well with small biases on individual parameters. Imprecision on individual parameters was comparable across software and showed marked improvements with increasing landmark time. In terms of coverage, results were also comparable and all software were able to well predict PSA kinetics and survival. As for computing time, Stan was faster than Monolix and NONMEM to obtain individual parameters. Stan 2.18, Monolix 2018R2 and NONMEM 7.4 are able to characterize IDP of biomarkers and risk of event in a nonlinear joint modelling framework with correct uncertainty and hence could be used in the context of individualized medicine.
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Affiliation(s)
| | - France Mentre
- Université de Paris, IAME, INSERM , F-75018, Paris, France
| | | | - Julie Bertrand
- Université de Paris, IAME, INSERM , F-75018, Paris, France
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7
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Green BJ, Nguyen V, Atenafu E, Weeber P, Duong BTV, Thiagalingam P, Labib M, Mohamadi RM, Hansen AR, Joshua AM, Kelley SO. Phenotypic Profiling of Circulating Tumor Cells in Metastatic Prostate Cancer Patients Using Nanoparticle-Mediated Ranking. Anal Chem 2019; 91:9348-9355. [DOI: 10.1021/acs.analchem.9b01697] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Brenda J. Green
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, 144 College Street, Toronto, Ontario M5S 3M2, Canada
| | - Vivian Nguyen
- Department of Pharmaceutical Sciences, University of Toronto, Toronto M5S 3M2, Canada
| | - Eshetu Atenafu
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2C1, Canada
| | - Phillip Weeber
- Department of Pharmaceutical Sciences, University of Toronto, Toronto M5S 3M2, Canada
| | - Bill T. V. Duong
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Punithan Thiagalingam
- Department of Pharmaceutical Sciences, University of Toronto, Toronto M5S 3M2, Canada
| | - Mahmoud Labib
- Department of Pharmaceutical Sciences, University of Toronto, Toronto M5S 3M2, Canada
| | - Reza M. Mohamadi
- Department of Pharmaceutical Sciences, University of Toronto, Toronto M5S 3M2, Canada
| | - Aaron R. Hansen
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2C1, Canada
| | - Anthony M. Joshua
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2C1, Canada
- Kinghorn Cancer Centre, St. Vincent’s Hospital Sydney, Darlinghurst, New South Wales 2010, Australia
| | - Shana O. Kelley
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, 144 College Street, Toronto, Ontario M5S 3M2, Canada
- Department of Pharmaceutical Sciences, University of Toronto, Toronto M5S 3M2, Canada
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- Department of Biochemistry, Faculty of Medicine, University of Toronto, Toronto, Ontario M5S 1A8, Canada
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Abstract
Mangroves are unique coastal forest ecosystem distributed along the tropical and subtropical region of the world. They are evolutionarily adapted to combat against hostile environmental conditions such as low oxygen, high salinity, and temperature. The adaptive features endowed with novel secondary metabolic pathways and bioactive compounds to sustain in harsh conditions. The novel metabolites are a rich source of the wide range of bioactive compounds and natural products. It includes terpenoids, alkaloids, phenolics, saponins, flavonoids, and steroids. The bioactive and natural compounds may serve as therapeutic precursors and industrial raw materials. Terpenes and polyphenols have antiviral, antibacterial, antifungal, antimalarial, anticancer or combination of activities. To date, several mangroves plants were examined and recognized as a potential source of novel natural product for exploitation in medicine. In fact, most of the isolated compounds are novel and showed promising biological activities such as gastroprotective, cytotoxic, antioxidant, antibacterial, antifungal, antiviral, enzyme activation and inhibition, immunosuppressive, anti-inflammatory, antifeedant effects. In the present review, we have compiled the achievements and progress in mangroves natural products research of the last decade.
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Affiliation(s)
- Nilesh Lakshman Dahibhate
- Department of Biological Sciences, Birla Institute of Technology and Science Pilani, K. K. Birla Goa Campus, Sancoale, Goa 403726, India
| | - Ankush Ashok Saddhe
- Department of Biological Sciences, Birla Institute of Technology and Science Pilani, K. K. Birla Goa Campus, Sancoale, Goa 403726, India
| | - Kundan Kumar
- Department of Biological Sciences, Birla Institute of Technology and Science Pilani, K. K. Birla Goa Campus, Sancoale, Goa 403726, India
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Gabrielsson J, Andersson R, Jirstrand M, Hjorth S. Dose-Response-Time Data Analysis: An Underexploited Trinity. Pharmacol Rev 2018; 71:89-122. [DOI: 10.1124/pr.118.015750] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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10
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Nair S, LLerena A. New perspectives in personalised medicine for ethnicity in cancer: population pharmacogenomics and pharmacometrics. Drug Metab Pers Ther 2018; 33:61-64. [PMID: 29688886 DOI: 10.1515/dmpt-2018-0008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Sujit Nair
- Amrita Cancer Discovery Biology Laboratory, Amrita Vishwa Vidyapeetham University, Amritapuri, Clappana P.O., Kollam - 690525, Kerala, India
| | - Adrián LLerena
- CICAB Clinical Research Centre at Extremadura University Hospital and Medical School, Universidad de Extremadura, Badajoz, Spain
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11
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Challenging the dose-response-time data approach: Analysis of a complex system. Eur J Pharm Sci 2018; 128:250-269. [PMID: 30453011 DOI: 10.1016/j.ejps.2018.11.015] [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: 05/06/2018] [Revised: 10/23/2018] [Accepted: 11/14/2018] [Indexed: 11/23/2022]
Abstract
This study presents an extensive dose-response-time (DRT) meta-analysis of the nicotinic acid-induced inhibition of free fatty acids and insulin release. The purpose was to quantify the implications of lacking exposure data when analysing complex pharmacodynamic systems. The DRT model successfully characterised various response behaviours-including time-delays, rebound, feedback mechanisms, and adaptation-on both the individual and the population level. Comparing the fitted DRT model to an exposure-driven reference analysis showed that bias and uncertainty were introduced in the parameter estimates. However, most estimates were within one standard error from the reference. In both approaches, a few parameters suffered from practical identifiability issues, likely due to large differences in half-lives of the different rate processes. Moreover, the optimal dosing strategies predicted by the DRT model differed slightly from those of the exposure-driven analysis, having a lower optimal steady-state reduction of free fatty acids exposure.
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12
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Liquid Biopsy Potential Biomarkers in Prostate Cancer. Diagnostics (Basel) 2018; 8:diagnostics8040068. [PMID: 30698162 PMCID: PMC6316409 DOI: 10.3390/diagnostics8040068] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 09/12/2018] [Accepted: 09/14/2018] [Indexed: 12/12/2022] Open
Abstract
Prostate cancer (PCa) is the second most common cancer in men worldwide with an incidence of 14.8% and a mortality of 6.6%. Shortcomings in comprehensive medical check-ups in low- and middle-income countries lead to delayed detection of PCa and are causative of high numbers of advanced PCa cases at first diagnosis. The performance of available biomarkers is still insufficient and limited applicability, including logistical and financial burdens, impedes comprehensive implementation into health care systems. There is broad agreement on the need of new biomarkers to improve (i) early detection of PCa, (ii) risk stratification, (iii) prognosis, and (iv) treatment monitoring. This review focuses on liquid biopsy tests distinguishing high-grade significant (Gleason score (GS) ≥ 7) from low-grade indolent PCa. Available biomarkers still lack performance in risk stratification of biopsy naïve patients. However, biomarkers with highly negative predictive values may help to reduce unnecessary biopsies. Risk calculators using integrative scoring systems clearly improve decision-making for invasive prostate biopsy. Emerging biomarkers have the potential to substitute PSA and improve the overall performance of risk calculators. Until then, PSA should be used and may be replaced whenever enough evidence has accumulated for better performance of a new biomarker.
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Hecht M, Veigure R, Couchman L, S Barker CI, Standing JF, Takkis K, Evard H, Johnston A, Herodes K, Leito I, Kipper K. Utilization of data below the analytical limit of quantitation in pharmacokinetic analysis and modeling: promoting interdisciplinary debate. Bioanalysis 2018; 10:1229-1248. [PMID: 30033744 DOI: 10.4155/bio-2018-0078] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Traditionally, bioanalytical laboratories do not report actual concentrations for samples with results below the LOQ (BLQ) in pharmacokinetic studies. BLQ values are outside the method calibration range established during validation and no data are available to support the reliability of these values. However, ignoring BLQ data can contribute to bias and imprecision in model-based pharmacokinetic analyses. From this perspective, routine use of BLQ data would be advantageous. We would like to initiate an interdisciplinary debate on this important topic by summarizing the current concepts and use of BLQ data by regulators, pharmacometricians and bioanalysts. Through introducing the limit of detection and evaluating its variability, BLQ data could be released and utilized appropriately for pharmacokinetic research.
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Affiliation(s)
- Max Hecht
- Chair of Analytical Chemistry, Institute of Chemistry, University of Tartu, 14a Ravila Street, 50411 Tartu, Estonia
- Analytical Services International, St George's University of London, Cranmer Terrace, London, SW17 0RE, UK
| | - Rūta Veigure
- Chair of Analytical Chemistry, Institute of Chemistry, University of Tartu, 14a Ravila Street, 50411 Tartu, Estonia
| | - Lewis Couchman
- Analytical Services International, St George's University of London, Cranmer Terrace, London, SW17 0RE, UK
| | - Charlotte I S Barker
- Paediatric Infectious Diseases Research Group, Institute for Infection & Immunity, St George's University of London, London, SW17 0RE, UK
- Inflammation, Infection & Rheumatology Section, UCL Great Ormond Street Institute of Child Health, London, WC1N 1EH, UK
- Paediatric Infectious Diseases Unit, St George's University Hospitals NHS Foundation Trust, London, SW17 0RE, UK
| | - Joseph F Standing
- Paediatric Infectious Diseases Research Group, Institute for Infection & Immunity, St George's University of London, London, SW17 0RE, UK
- Inflammation, Infection & Rheumatology Section, UCL Great Ormond Street Institute of Child Health, London, WC1N 1EH, UK
| | - Kalev Takkis
- Analytical Services International, St George's University of London, Cranmer Terrace, London, SW17 0RE, UK
| | - Hanno Evard
- Chair of Analytical Chemistry, Institute of Chemistry, University of Tartu, 14a Ravila Street, 50411 Tartu, Estonia
| | - Atholl Johnston
- Analytical Services International, St George's University of London, Cranmer Terrace, London, SW17 0RE, UK
- Clinical Pharmacology, Barts & The London School of Medicine & Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Koit Herodes
- Chair of Analytical Chemistry, Institute of Chemistry, University of Tartu, 14a Ravila Street, 50411 Tartu, Estonia
| | - Ivo Leito
- Chair of Analytical Chemistry, Institute of Chemistry, University of Tartu, 14a Ravila Street, 50411 Tartu, Estonia
| | - Karin Kipper
- Chair of Analytical Chemistry, Institute of Chemistry, University of Tartu, 14a Ravila Street, 50411 Tartu, Estonia
- Analytical Services International, St George's University of London, Cranmer Terrace, London, SW17 0RE, UK
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14
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Nair S, Kong ANT. Emerging roles for clinical pharmacometrics in cancer precision medicine. ACTA ACUST UNITED AC 2018; 4:276-283. [PMID: 30345221 DOI: 10.1007/s40495-018-0139-0] [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] [Indexed: 12/23/2022]
Abstract
Purpose of review Although significant progress has been made in cancer research, there exist unmet needs in patient care as reflected by the 'Cancer Moonshot' goals. This review appreciates the potential utility of quantitative pharmacology in cancer precision medicine. Recent findings Precision oncology has received federal funding largely due to 'The Precision Medicine Initiative'. Precision medicine takes into account the inter-individual variability, and allows for tailoring the right medication or the right dose of drug to the best subpopulation of patients who will likely respond to the intervention, thus enhancing therapeutic success and reducing "financial toxicity" to patients, families and caregivers. The National Cancer Institute (NCI) committed US$ 70 million from its fiscal year 2016 budget to advance precision oncology research. Through the 'Critical Path Initiative', pharmacometrics has gained an important role in drug development; however, it is yet to find widespread clinical applicability. Summary Stakeholders including clinicians and pharmacometricians need to work in concert to ensure that benefits of model-based approaches are harnessed to personalize cancer care to the individual needs of the patient via better dosing strategies, companion diagnostics, and predictive biomarkers. In medical oncology, where immediate patient care is the clinician's primary concern, pharmacometric approaches can be tailored to build models that rely on patient data already digitally available in the Electronic Health Record (EHR) to facilitate quick collaboration and avoid additional funding needs. Taken together, we offer a roadmap for the future of precision oncology which is fraught with both challenges and opportunities for pharmacometricians and clinicians alike.
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Affiliation(s)
- Sujit Nair
- Amrita Cancer Discovery Biology Laboratory, Amrita Vishwa Vidyapeetham University, Amritapuri, Clappana P.O., Kollam - 690525, Kerala, India
| | - Ah-Ng Tony Kong
- Center for Cancer Chemoprevention Research and Department of Pharmaceutics, Rutgers, The State University of New Jersey, 160 Frelinghuysen Road, Piscataway, NJ-08854, USA
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15
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Lavezzi SM, Borella E, Carrara L, De Nicolao G, Magni P, Poggesi I. Mathematical modeling of efficacy and safety for anticancer drugs clinical development. Expert Opin Drug Discov 2017; 13:5-21. [DOI: 10.1080/17460441.2018.1388369] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Silvia Maria Lavezzi
- Dipartimento di Ingegneria Industriale e dell’Informazione, Università degli Studi di Pavia, Pavia, Italy
| | - Elisa Borella
- Dipartimento di Ingegneria Industriale e dell’Informazione, Università degli Studi di Pavia, Pavia, Italy
| | - Letizia Carrara
- Dipartimento di Ingegneria Industriale e dell’Informazione, Università degli Studi di Pavia, Pavia, Italy
| | - Giuseppe De Nicolao
- Dipartimento di Ingegneria Industriale e dell’Informazione, Università degli Studi di Pavia, Pavia, Italy
| | - Paolo Magni
- Dipartimento di Ingegneria Industriale e dell’Informazione, Università degli Studi di Pavia, Pavia, Italy
| | - Italo Poggesi
- Global Clinical Pharmacology, Janssen Research and Development, Cologno Monzese, Italy
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16
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Liu W, Yin B, Wang X, Yu P, Duan X, Liu C, Wang B, Tao Z. Circulating tumor cells in prostate cancer: Precision diagnosis and therapy. Oncol Lett 2017; 14:1223-1232. [PMID: 28789337 PMCID: PMC5529747 DOI: 10.3892/ol.2017.6332] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2016] [Accepted: 03/09/2017] [Indexed: 12/14/2022] Open
Abstract
The primary cause of tumor-associated mortality in prostate cancer (PCa) remains distant metastasis. The dissemination of tumor cells from the primary tumor to distant sites through the bloodstream cannot be detected early by standard imaging methods. Circulating tumor cells (CTCs) represent an effective prognostic and predictive biomarker, which are able to monitor efficacy of adjuvant therapies, detect early development of metastases, and finally, assess therapeutic responses of advanced disease earlier than traditional diagnostic methods. In addition, since repeated tissue biopsies are invasive, costly and not always feasible, the assessment of tumor characteristics on CTCs, by a peripheral blood sample as a liquid biopsy, represents an attractive opportunity. The implementation of molecular and genomic characterization of CTCs may contribute to improve the treatment selection and thus, to move toward more precise diagnosis and therapy in PCa. The present study summarizes the current advances in CTC enrichment and detection strategies and reviews how CTCs may contribute to significant insights in the metastatic process, as well as how they may be utilized in clinical application in PCa. Although it is proposed that CTCs may offer insights into the prognosis and management of PCa, there are a number of challenges in the study of circulating tumor cells, and their clinical utility remains under investigation.
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Affiliation(s)
- Weiwei Liu
- Department of Laboratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, P.R. China
| | - Binbin Yin
- Department of Laboratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, P.R. China
| | - Xuchu Wang
- Department of Laboratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, P.R. China
| | - Pan Yu
- Department of Laboratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, P.R. China
| | - Xiuzhi Duan
- Department of Laboratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, P.R. China
| | - Chunhua Liu
- Department of Laboratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, P.R. China
| | - Ben Wang
- Department of Laboratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, P.R. China
| | - Zhihua Tao
- Department of Laboratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, P.R. China
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Desmée S, Mentré F, Veyrat-Follet C, Sébastien B, Guedj J. Nonlinear joint models for individual dynamic prediction of risk of death using Hamiltonian Monte Carlo: application to metastatic prostate cancer. BMC Med Res Methodol 2017; 17:105. [PMID: 28716060 PMCID: PMC5513366 DOI: 10.1186/s12874-017-0382-9] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 06/30/2017] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Joint models of longitudinal and time-to-event data are increasingly used to perform individual dynamic prediction of a risk of event. However the difficulty to perform inference in nonlinear models and to calculate the distribution of individual parameters has long limited this approach to linear mixed-effect models for the longitudinal part. Here we use a Bayesian algorithm and a nonlinear joint model to calculate individual dynamic predictions. We apply this approach to predict the risk of death in metastatic castration-resistant prostate cancer (mCRPC) patients with frequent Prostate-Specific Antigen (PSA) measurements. METHODS A joint model is built using a large population of 400 mCRPC patients where PSA kinetics is described by a biexponential function and the hazard function is a PSA-dependent function. Using Hamiltonian Monte Carlo algorithm implemented in Stan software and the estimated population parameters in this population as priors, the a posteriori distribution of the hazard function is computed for a new patient knowing his PSA measurements until a given landmark time. Time-dependent area under the ROC curve (AUC) and Brier score are derived to assess discrimination and calibration of the model predictions, first on 200 simulated patients and then on 196 real patients that are not included to build the model. RESULTS Satisfying coverage probabilities of Monte Carlo prediction intervals are obtained for longitudinal and hazard functions. Individual dynamic predictions provide good predictive performances for landmark times larger than 12 months and horizon time of up to 18 months for both simulated and real data. CONCLUSIONS As nonlinear joint models can characterize the kinetics of biomarkers and their link with a time-to-event, this approach could be useful to improve patient's follow-up and the early detection of most at risk patients.
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Affiliation(s)
- Solène Desmée
- IAME, UMR 1137, INSERM, Université Paris Diderot, Sorbonne Paris Cité, 16, rue Henri Huchard, Paris, 75018 France
| | - France Mentré
- IAME, UMR 1137, INSERM, Université Paris Diderot, Sorbonne Paris Cité, 16, rue Henri Huchard, Paris, 75018 France
| | | | | | - Jérémie Guedj
- IAME, UMR 1137, INSERM, Université Paris Diderot, Sorbonne Paris Cité, 16, rue Henri Huchard, Paris, 75018 France
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18
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Desmée S, Mentré F, Veyrat-Follet C, Sébastien B, Guedj J. Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients. Biometrics 2016; 73:305-312. [PMID: 27148956 DOI: 10.1111/biom.12537] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Revised: 02/01/2016] [Accepted: 03/01/2016] [Indexed: 01/08/2023]
Abstract
Joint modeling is increasingly popular for investigating the relationship between longitudinal and time-to-event data. However, numerical complexity often restricts this approach to linear models for the longitudinal part. Here, we use a novel development of the Stochastic-Approximation Expectation Maximization algorithm that allows joint models defined by nonlinear mixed-effect models. In the context of chemotherapy in metastatic prostate cancer, we show that a variety of patterns for the Prostate Specific Antigen (PSA) kinetics can be captured by using a mechanistic model defined by nonlinear ordinary differential equations. The use of a mechanistic model predicts that biological quantities that cannot be observed, such as treatment-sensitive and treatment-resistant cells, may have a larger impact than PSA value on survival. This suggests that mechanistic joint models could constitute a relevant approach to evaluate the efficacy of treatment and to improve the prediction of survival in patients.
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Affiliation(s)
- Solène Desmée
- INSERM, IAME, UMR 1137, F-75018 Paris, France.,Université Paris Diderot, IAME, UMR 1137, Sorbonne Paris Cité, F-75018 Paris, France
| | - France Mentré
- INSERM, IAME, UMR 1137, F-75018 Paris, France.,Université Paris Diderot, IAME, UMR 1137, Sorbonne Paris Cité, F-75018 Paris, France
| | - Christine Veyrat-Follet
- Drug Disposition, Disposition Safety and Animal Research Department, Sanofi, Alfortville, France
| | | | - Jérémie Guedj
- INSERM, IAME, UMR 1137, F-75018 Paris, France.,Université Paris Diderot, IAME, UMR 1137, Sorbonne Paris Cité, F-75018 Paris, France
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Buil-Bruna N, López-Picazo JM, Martín-Algarra S, Trocóniz IF. Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications. Oncologist 2015; 21:220-32. [PMID: 26668254 DOI: 10.1634/theoncologist.2015-0322] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Accepted: 11/03/2015] [Indexed: 11/17/2022] Open
Abstract
UNLABELLED Despite much investment and progress, oncology is still an area with significant unmet medical needs, with new therapies and more effective use of current therapies needed. The emergent field of pharmacometrics combines principles from pharmacology (pharmacokinetics [PK] and pharmacodynamics [PD]), statistics, and computational modeling to support drug development and optimize the use of already marketed drugs. Although it has gained a role within drug development, its use in clinical practice remains scarce. The aim of the present study was to review the principal pharmacometric concepts and provide some examples of its use in oncology. Integrated population PK/PD/disease progression models as part of the pharmacometrics platform provide a powerful tool to predict outcomes so that the right dose can be given to the right patient to maximize drug efficacy and reduce drug toxicity. Population models often can be developed with routinely collected medical record data; therefore, we encourage the application of such models in the clinical setting by generating close collaborations between physicians and pharmacometricians. IMPLICATIONS FOR PRACTICE The present review details how the emerging field of pharmacometrics can integrate medical record data with predictive pharmacological and statistical models of drug response to optimize and individualize therapies. In order to make this routine practice in the clinic, greater awareness of the potential benefits of the field is required among clinicians, together with closer collaboration between pharmacometricians and clinicians to ensure the requisite data are collected in a suitable format for pharmacometrics analysis.
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Affiliation(s)
- Núria Buil-Bruna
- Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - José-María López-Picazo
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain Department of Medical Oncology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Salvador Martín-Algarra
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain Department of Medical Oncology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
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