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Polasek TM, Peck RW. Beyond Population-Level Targets for Drug Concentrations: Precision Dosing Needs Individual-Level Targets that Include Superior Biomarkers of Drug Responses. Clin Pharmacol Ther 2024; 116:602-612. [PMID: 38328977 DOI: 10.1002/cpt.3197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 01/17/2024] [Indexed: 02/09/2024]
Abstract
The purpose of precision dosing is to increase the chances of therapeutic success in individual patients. This is achieved in practice by adjusting doses to reach precision dosing targets determined previously in relevant populations, ideally with robust supportive evidence showing improved clinical outcomes compared with standard dosing. But is this implicit assumption of translatable population-level precision dosing targets correct and the best for all patients? In this review, the types of precision dosing targets and how they are determined are outlined, problems with the translatability of these targets to individual patients are identified, and ways forward to address these challengers are proposed. Achieving improved clinical outcomes to support precision dosing over standard dosing is currently hampered by applying population-level targets to all patients. Just as "one-dose-fits-all" may be an inappropriate philosophy for drug treatment overall, a "one-target-fits-all" philosophy may limit the broad clinical benefits of precision dosing. Defining individual-level precision dosing targets may be needed for greatest therapeutic success. Superior future precision dosing targets will integrate several biomarkers that together account for the multiple sources of drug response variability.
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Affiliation(s)
- Thomas M Polasek
- Centre for Medicine Use and Safety, Monash University, Melbourne, Victoria, Australia
- CMAX Clinical Research, Adelaide, South Australia, Australia
| | - Richard W Peck
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
- Pharma Research & Development (pRED), Roche Innovation Center Basel, Basel, Switzerland
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Alsultan A, Dasuqi SA, Almohaizeie A, Aljutayli A, Aljamaan F, Omran RA, Alolayan A, Hamad MA, Alotaibi H, Altamimi S, Alghanem SS. External Validation of Obese/Critically Ill Vancomycin Population Pharmacokinetic Models in Critically Ill Patients Who Are Obese. J Clin Pharmacol 2024; 64:353-361. [PMID: 37862131 DOI: 10.1002/jcph.2375] [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: 07/27/2023] [Accepted: 10/17/2023] [Indexed: 10/22/2023]
Abstract
Obesity combined with critical illness might increase the risk of acquiring infections and hence mortality. In this patient population the pharmacokinetics of antimicrobials vary significantly, making antimicrobial dosing challenging. The objective of this study was to assess the predictive performance of published population pharmacokinetic models of vancomycin in patients who are critically ill or obese for a cohort of critically ill patients who are obese. This was a multi-center retrospective study conducted at 2 hospitals. Adult patients with a body mass index of ≥30 kg/m2 were included. PubMed was searched for published population pharmacokinetic studies in patients who were critically ill or obese. External validation was performed using Monolix software. A total of 4 models were identified in patients who were obese and 5 models were identified in patients who were critically ill. In total, 138 patients who were critically ill and obese were included, and the most accurate models for these patients were the Goti and Roberts models. In our analysis, models in patients who were critically ill outperformed models in patients who were obese. When looking at the most accurate models, both the Goti and the Roberts models had patient characteristics similar to ours in terms of age and creatinine clearance. This indicates that when selecting the proper model to apply in practice, it is important to account for all relevant variables, besides obesity.
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Affiliation(s)
- Abdullah Alsultan
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Shereen A Dasuqi
- Department of Pharmacy, King Khalid University Hospital, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Abdullah Almohaizeie
- Pharmaceutical Care Division, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Abdullah Aljutayli
- Department of Pharmaceutics, Faculty of Pharmacy, Qassim University, Riyadh, Saudi Arabia
| | - Fadi Aljamaan
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Critical Care Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
| | - Rasha A Omran
- Department of Pharmaceutics and Pharmaceutical Technology, School of Pharmacy, University of Jordan, Amman, Jordan
| | - Abdulaziz Alolayan
- Pharmacy Department, Prince Sultan Military Medical City, Riyadh, Kingdom of Saudi Arabia, Riyadh, Saudi Arabia
| | - Mohammed A Hamad
- Critical Care Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
- Department of Acute Medicine, Wirral University Teaching Hospital NHS Foundation Trust, Arrowe Park Hospital, Wirral, UK
| | - Haifa Alotaibi
- Pharmaceutical Care Division, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Sarah Altamimi
- Pharmaceutical Care Division, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Sarah S Alghanem
- Department of Pharmacy Practice, College of Pharmacy at Kuwait University, Safat, Kuwait
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Oda K, Yamada T, Matsumoto K, Hanai Y, Ueda T, Samura M, Shigemi A, Jono H, Saito H, Kimura T. Model-informed precision dosing of vancomycin for rapid achievement of target area under the concentration-time curve: A simulation study. Clin Transl Sci 2023; 16:2265-2275. [PMID: 37718491 PMCID: PMC10651648 DOI: 10.1111/cts.13626] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/19/2023] Open
Abstract
In this study, we aimed to evaluate limited sampling strategies for achieving the therapeutic ranges of the area under the concentration-time curve (AUC) of vancomycin on the first and second day (AUC0-24 , AUC24-48 , respectively) of therapy. A virtual population of 1000 individuals was created using a population pharmacokinetic (PopPK) model, which was validated and incorporated into our model-informed precision dosing tool. The results were evaluated using six additional PopPK models selected based on a study design of prospective or retrospective data collection with sufficient concentrations. Bayesian forecasting was performed to evaluate the probability of achieving the therapeutic range of AUC, defined as a ratio of estimated/reference AUC within 0.8-1.2. The Bayesian posterior probability of achieving the AUC24-48 range increased from 51.3% (a priori probability) to 77.5% after using two-point sampling at the trough and peak on the first day. Sampling on the first day also yielded a higher Bayesian posterior probability (86.1%) of achieving the AUC0-24 range compared to the a priori probability of 60.1%. The Bayesian posterior probability of achieving the AUC at steady-state (AUCSS ) range by sampling on the first or second day decreased with decreased kidney function. We demonstrated that second-day trough and peak sampling provided accurate AUC24-48 , and first-day sampling may assist in rapidly achieving therapeutic AUC24-48 , although the AUCSS should be re-estimated in patients with reduced kidney function owing to its unreliable predictive performance.
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Affiliation(s)
- Kazutaka Oda
- Department of PharmacyKumamoto University HospitalKumamotoJapan
- Department of Infection ControlKumamoto University HospitalKumamotoJapan
| | - Tomoyuki Yamada
- Department of PharmacyOsaka Medical and Pharmaceutical University HospitalOsakaJapan
| | - Kazuaki Matsumoto
- Division of PharmacodynamicsKeio University Faculty of PharmacyTokyoJapan
| | - Yuki Hanai
- Department of Clinical Pharmacy, Faculty of Pharmaceutical SciencesToho UniversityChibaJapan
| | - Takashi Ueda
- Department of Infection Control and PreventionHyogo College of MedicineNishinomiyaHyogoJapan
| | - Masaru Samura
- Department of PharmacyYokohama General HospitalYokohamaKanagawaJapan
| | - Akari Shigemi
- Department of PharmacyKagoshima University HospitalKagoshima CityKagoshimaJapan
| | - Hirofumi Jono
- Department of PharmacyKumamoto University HospitalKumamotoJapan
| | - Hideyuki Saito
- Department of PharmacyKumamoto University HospitalKumamotoJapan
| | - Toshimi Kimura
- Department of PharmacyJuntendo University HospitalTokyoJapan
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Polasek TM. Virtual twin for healthcare management. Front Digit Health 2023; 5:1246659. [PMID: 37781454 PMCID: PMC10540783 DOI: 10.3389/fdgth.2023.1246659] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/01/2023] [Indexed: 10/03/2023] Open
Abstract
Healthcare is increasingly fragmented, resulting in escalating costs, patient dissatisfaction, and sometimes adverse clinical outcomes. Strategies to decrease healthcare fragmentation are therefore attractive from payer and patient perspectives. In this commentary, a patient-centered smart phone application called Virtual Twin for Healthcare Management (VTHM) is proposed, including its organizational layout, basic functionality, and potential clinical applications. The platform features a virtual twin hub that displays the body and its health data. This is a physiologically based human model that is "virtualized" for the patient based on their unique genetic, molecular, physiological, and disease characteristics. The spokes of the system are a full service and interoperable electronic-health record, accessible to healthcare providers with permission on any device with internet access. Theoretical case studies based on real scenarios are presented to show how VTHM could potentially improve patient care and clinical efficiency. Challenges that must be overcome to turn VTHM into reality are also briefly outlined. Notably, the VTHM platform is designed to operationalize current and future precision medicine initiatives, such as access to molecular diagnostic results, pharmacogenomics-guided prescribing, and model-informed precision dosing.
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Affiliation(s)
- Thomas M. Polasek
- Certara, Princeton, NJ, United States
- Centre for Medicines Use and Safety, Monash University, Melbourne, VIC, Australia
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Kim R, Jayanti RP, Lee H, Kim HK, Kang J, Park IN, Kim J, Oh JY, Kim HW, Lee H, Ghim JL, Ahn S, Long NP, Cho YS, Shin JG. Development of a population pharmacokinetic model of pyrazinamide to guide personalized therapy: impacts of geriatric and diabetes mellitus on clearance. Front Pharmacol 2023; 14:1116226. [PMID: 37305528 PMCID: PMC10250603 DOI: 10.3389/fphar.2023.1116226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 04/19/2023] [Indexed: 06/13/2023] Open
Abstract
Objectives: This study was performed to develop a population pharmacokinetic model of pyrazinamide for Korean tuberculosis (TB) patients and to explore and identify the influence of demographic and clinical factors, especially geriatric diabetes mellitus (DM), on the pharmacokinetics (PK) of pyrazinamide (PZA). Methods: PZA concentrations at random post-dose points, demographic characteristics, and clinical information were collected in a multicenter prospective TB cohort study from 18 hospitals in Korea. Data obtained from 610 TB patients were divided into training and test datasets at a 4:1 ratio. A population PK model was developed using a nonlinear mixed-effects method. Results: A one-compartment model with allometric scaling for body size effect adequately described the PK of PZA. Geriatric patients with DM (age >70 years) were identified as a significant covariate, increasing the apparent clearance of PZA by 30% (geriatric patients with DM: 5.73 L/h; others: 4.50 L/h), thereby decreasing the area under the concentration-time curve from 0 to 24 h by a similar degree compared with other patients (geriatric patients with DM: 99.87 μg h/mL; others: 132.3 μg h/mL). Our model was externally evaluated using the test set and provided better predictive performance compared with the previously published model. Conclusion: The established population PK model sufficiently described the PK of PZA in Korean TB patients. Our model will be useful in therapeutic drug monitoring to provide dose optimization of PZA, particularly for geriatric patients with DM and TB.
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Affiliation(s)
- Ryunha Kim
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
| | - Rannissa Puspita Jayanti
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
| | - Hongyeul Lee
- Division of Pulmonary, Critical Care Medicine, Department of Internal Medicine, Inje University College of Medicine, Busan Paik Hospital, Busan, Republic of Korea
| | - Hyun-Kuk Kim
- Division of Pulmonology, Department of Internal Medicine, Inje University Haeundae Paik Hospital, Busan, Republic of Korea
| | - Jiyeon Kang
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Inje University Ilsan Paik Hospital, Goyang-si, Republic of Korea
| | - I-Nae Park
- Department of Internal Medicine, Inje University Seoul Paik Hospital, Inje University College of Medicine, Seoul, Republic of Korea
| | - Jehun Kim
- Pulmonary Division, Department of IM, Kosin University Gospel Hospital, Busan, Republic of Korea
| | - Jee Youn Oh
- Division of Pulmonology, Department of Internal Medicine, Korea University Guro Hospital, Seoul, Republic of Korea
| | - Hyung Woo Kim
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Incheon, Republic of Korea
| | - Heayon Lee
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jong-Lyul Ghim
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
| | - Sangzin Ahn
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
| | - Nguyen Phuoc Long
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
| | - Yong-Soon Cho
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
| | - Jae-Gook Shin
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
- Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan, Republic of Korea
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Oda K, Saito H, Jono H. Bayesian prediction-based individualized dosing of anti-methicillin-resistant Staphylococcus aureus treatment: Recent advancements and prospects in therapeutic drug monitoring. Pharmacol Ther 2023; 246:108433. [PMID: 37149156 DOI: 10.1016/j.pharmthera.2023.108433] [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: 12/26/2022] [Revised: 04/19/2023] [Accepted: 05/02/2023] [Indexed: 05/08/2023]
Abstract
As one of the efficient techniques for TDM, the population pharmacokinetic (popPK) model approach for dose individualization has been developed due to the rapidly growing innovative progress in computer technology and has recently been considered as a part of model-informed precision dosing (MIPD). Initial dose individualization and measurement followed by maximum a posteriori (MAP)-Bayesian prediction using a popPK model are the most classical and widely used approach among a class of MIPD strategies. MAP-Bayesian prediction offers the possibility of dose optimization based on measurement even before reaching a pharmacokinetically steady state, such as in an emergency, especially for infectious diseases requiring urgent antimicrobial treatment. As the pharmacokinetic processes in critically ill patients are affected and highly variable due to pathophysiological disturbances, the advantages offered by the popPK model approach make it highly recommended and required for effective and appropriate antimicrobial treatment. In this review, we focus on novel insights and beneficial aspects of the popPK model approach, especially in the treatment of infectious diseases with anti-methicillin-resistant Staphylococcus aureus agents represented by vancomycin, and discuss the recent advancements and prospects in TDM practice.
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Affiliation(s)
- Kazutaka Oda
- Department of Pharmacy, Kumamoto University Hospital, 1-1-1 Honjo, Chuo-ku, Kumamoto, Japan
| | - Hideyuki Saito
- Department of Pharmacy, Kumamoto University Hospital, 1-1-1 Honjo, Chuo-ku, Kumamoto, Japan; Department of Clinical Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, Kumamoto University; 1-1-1, Honjo, Chuo-ku, Kumamoto, Japan
| | - Hirofumi Jono
- Department of Pharmacy, Kumamoto University Hospital, 1-1-1 Honjo, Chuo-ku, Kumamoto, Japan; Department of Clinical Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, Kumamoto University; 1-1-1, Honjo, Chuo-ku, Kumamoto, Japan.
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Soedarsono S, Jayanti RP, Mertaniasih NM, Kusmiati T, Permatasari A, Indrawanto DW, Charisma AN, Lius EE, Yuliwulandari R, Quang Hoa P, Ky Phat N, Thu VTA, Ky Anh N, Ahn S, Phuoc Long N, Cho YS, Shin JG. Development of population pharmacokinetics model and Bayesian estimation of rifampicin exposure in Indonesian patients with tuberculosis. Tuberculosis (Edinb) 2023; 139:102325. [PMID: 36841141 DOI: 10.1016/j.tube.2023.102325] [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: 09/06/2022] [Revised: 01/04/2023] [Accepted: 02/12/2023] [Indexed: 02/16/2023]
Abstract
BACKGROUND Interindividual variability in the pharmacokinetics (PK) of anti-tuberculosis (TB) drugs is the leading cause of treatment failure. Herein, we evaluated the influence of demographic, clinical, and genetic factors that cause variability in RIF PK parameters in Indonesian TB patients. METHODS In total, 210 Indonesian patients with TB (300 plasma samples) were enrolled in this study. Clinical data, solute carrier organic anion transporter family member-1B1 (SLCO1B1) haplotypes *1a, *1b, and *15, and RIF concentrations were analyzed. The population PK model was developed using a non-linear mixed effect method. RESULTS A one-compartment model with allometric scaling adequately described the PK of RIF. Age and SLCO1B1 haplotype *15 were significantly associated with variability in apparent clearance (CL/F). For patients in their 40s, each 10-year increase in age was associated with a 10% decrease in CL/F (7.85 L/h). Patients with the SLCO1B1 haplotype *15 had a 24% lower CL/F compared to those with the wild-type. Visual predictive checks and non-parametric bootstrap analysis indicated good model performance. CONCLUSION Age and SLCO1B1 haplotype *15 were significant covariates of RIF CL/F. Geriatric patients with haplotype *15 had significantly greater exposure to RIF. The model could optimize TB pharmacotherapy through its application in therapeutic drug monitoring (clinical trial no. NCT05280886).
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Affiliation(s)
- Soedarsono Soedarsono
- Department of Pulmonology & Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60131, Indonesia; Sub-pulmonology Department of Internal Medicine, Faculty of Medicine, Hang Tuah University, Surabaya, 60244, Indonesia; Institute of Tropical Disease, Universitas Airlangga, Surabaya, 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya, 60131, Indonesia.
| | - Rannissa Puspita Jayanti
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Ni Made Mertaniasih
- Institute of Tropical Disease, Universitas Airlangga, Surabaya, 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya, 60131, Indonesia; Department of Clinical Microbiology, Faculty of Medicine, Universitas Airlangga, Surabaya, 60131, Indonesia
| | - Tutik Kusmiati
- Department of Pulmonology & Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60131, Indonesia; Institute of Tropical Disease, Universitas Airlangga, Surabaya, 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya, 60131, Indonesia
| | - Ariani Permatasari
- Department of Pulmonology & Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60131, Indonesia; Institute of Tropical Disease, Universitas Airlangga, Surabaya, 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya, 60131, Indonesia
| | - Dwi Wahyu Indrawanto
- Department of Pulmonology & Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya, 60131, Indonesia
| | - Anita Nur Charisma
- Department of Pulmonology & Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya, 60131, Indonesia
| | - Elvina Elizabeth Lius
- Department of Pulmonology & Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya, 60131, Indonesia
| | - Rika Yuliwulandari
- Department of Pharmacology, Faculty of Medicine, YARSI University, Jakarta, 10510, Indonesia; Genetic Research Center, YARSI Research Institute, YARSI University, Jakarta, 10510, Indonesia
| | - Pham Quang Hoa
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Nguyen Ky Phat
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Vo Thuy Anh Thu
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Nguyen Ky Anh
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Sangzin Ahn
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Nguyen Phuoc Long
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Yong-Soon Cho
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea.
| | - Jae-Gook Shin
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan, 47392, Republic of Korea
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Challenges and Possible Solutions to Direct-Acting Oral Anticoagulants (DOACs) Dosing in Patients with Extreme Bodyweight and Renal Impairment. Am J Cardiovasc Drugs 2023; 23:9-17. [PMID: 36515822 DOI: 10.1007/s40256-022-00560-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/04/2022] [Indexed: 12/15/2022]
Abstract
This article aims to highlight the dosing issues of direct oral anticoagulants (DOACs) in patients with renal impairment and/or obesity in an attempt to develop solutions employing advanced data-driven techniques. DOACs have become widely accepted by clinicians worldwide because of their superior clinical profiles, more predictable pharmacokinetics, and hence more convenient dosing relative to other anticoagulants. However, the optimal dosing of DOACs in extreme bodyweight patients and patients with renal impairment is difficult to achieve using the conventional dosing approach. The standard dosing approach (fixed-dose) is based on limited data from clinical studies. The existing formulae (models) for determining the appropriate doses for these patient groups leads to suboptimal dosing. This problem of mis-dosing is worsened by the lack of standardized laboratory parameters for monitoring the exposure to DOACs in renal failure and extreme bodyweight patients. Model-informed precision dosing (MIPD) encompasses a range of techniques like machine learning and pharmacometrics modelling, which could uncover key variables and relationships as well as shed more light on the pharmacokinetics and pharmacodynamics of DOACs in patients with extreme bodyweight or renal impairment. Ultimately, this individualized approach-if implemented in clinical practice-could optimise dosing for the DOACs for better safety and efficacy.
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Schatz LM, Zoller M, Scharf C, Liebchen U. [Therapeutic drug monitoring and pharmacokinetic models as a strategy for rational antibiotic therapy in intensive care patients]. DIE ANAESTHESIOLOGIE 2022; 71:495-501. [PMID: 35925054 DOI: 10.1007/s00101-022-01150-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/23/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Antibiotic dosing in intensive care patients is complex due to pharmacokinetic (PK) alterations. The aim of this article is to illustrate the role of therapeutic drug monitoring (TDM) and PK models to individualize antibiotic dosing. MATERIAL AND METHODS Guidelines and recommendations are discussed in the context of clinical practice and the prerequisites for routine TDM of different antibiotics are presented. In addition, the benefits and limitations of TDM are discussed. The advantages and disadvantages of TDM and PK models are described and the resulting future options are presented. RESULTS In the clinical routine, the peak or trough concentrations of antibiotics in blood are measured depending on the antibiotic class. Prerequisites for a purposeful TDM are a coordinated blood sampling and a prompt reporting of findings. As target ranges are not uniformly defined following rules, dosage adjustments are difficult. The PK models offer a valid possibility to individualize the antibiotic therapy of intensive care patients. Areas of application are the calculation of the loading dose and the combination with TDM for treatment control. For whom and how often TDM is necessary and how it can best be combined with PK models or even replace them should be investigated in the future, in addition to evaluation of the optimal target area. CONCLUSION The routine use of TDM for antibiotics in intensive care patients is only effective under the abovementioned conditions. By combination with PK models the treatment could be optimized in the future.
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Affiliation(s)
- Lea Marie Schatz
- Institut für Klinische Pharmazie, Westfälische Wilhelms-Universität Münster, Münster, Deutschland
| | - Michael Zoller
- Klinik für Anästhesiologie, LMU Klinikum, Marchioninistr. 15, 81377, München, Deutschland
| | - Christina Scharf
- Klinik für Anästhesiologie, LMU Klinikum, Marchioninistr. 15, 81377, München, Deutschland
| | - Uwe Liebchen
- Klinik für Anästhesiologie, LMU Klinikum, Marchioninistr. 15, 81377, München, Deutschland.
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Uster DW, Wicha SG. Optimized sampling to estimate vancomycin drug exposure: Comparison of pharmacometric and equation-based approaches in a simulation-estimation study. CPT Pharmacometrics Syst Pharmacol 2022; 11:711-720. [PMID: 35259285 PMCID: PMC9197536 DOI: 10.1002/psp4.12782] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 02/24/2022] [Accepted: 02/25/2022] [Indexed: 12/31/2022] Open
Abstract
Vancomycin dosing should be accompanied by area under the concentration‐time curve (AUC)–guided dosing using model‐informed precision dosing software according to the latest guidelines. Although a peak plus a trough sample is considered the gold standard to determine the AUC, single‐sample strategies might be more economic. Yet, optimal sampling times for AUC determination of vancomycin have not been systematically evaluated. In the present study, automated one‐ or two‐sample strategies were systematically explored to estimate the AUC with a model averaging and a model selection algorithm. Both were compared with a conventional equation‐based approach in a simulation‐estimation study mimicking a heterogenous patient population (n = 6000). The optimal single‐sample timepoints were identified between 2–6.5 h post dose, with varying bias values between −2.9% and 1.0% and an imprecision of 23.3%–24.0% across the population pharmacokinetic approaches. Adding a second sample between 4.5–6.0 h improved the predictive performance (−1.7% to 0.0% bias, 17.6%–18.6% imprecision), although the difference in the two‐sampling strategies were minor. The equation‐based approach was always positively biased and hence inferior to the population pharmacokinetic approaches. In conclusion, the approaches always preferred samples to be drawn early in the profile (<6.5 h), whereas sampling of trough concentrations resulted in a higher imprecision. Furthermore, optimal sampling during the early treatment phase could already give sufficient time to individualize the second dose, which is likely unfeasible using trough sampling.
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Affiliation(s)
- David W Uster
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Hamburg, Germany
| | - Sebastian G Wicha
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Hamburg, Germany
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11
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Jayanti RP, Long NP, Phat NK, Cho YS, Shin JG. Semi-Automated Therapeutic Drug Monitoring as a Pillar toward Personalized Medicine for Tuberculosis Management. Pharmaceutics 2022; 14:pharmaceutics14050990. [PMID: 35631576 PMCID: PMC9147223 DOI: 10.3390/pharmaceutics14050990] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/26/2022] [Accepted: 05/02/2022] [Indexed: 12/10/2022] Open
Abstract
Standard tuberculosis (TB) management has failed to control the growing number of drug-resistant TB cases worldwide. Therefore, innovative approaches are required to eradicate TB. Model-informed precision dosing and therapeutic drug monitoring (TDM) have become promising tools for adjusting anti-TB drug doses corresponding with individual pharmacokinetic profiles. These are crucial to improving the treatment outcome of the patients, particularly for those with complex comorbidity and a high risk of treatment failure. Despite the actual benefits of TDM at the bedside, conventional TDM encounters several hurdles related to laborious, time-consuming, and costly processes. Herein, we review the current practice of TDM and discuss the main obstacles that impede it from successful clinical implementation. Moreover, we propose a semi-automated TDM approach to further enhance precision medicine for TB management.
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Affiliation(s)
- Rannissa Puspita Jayanti
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Korea; (R.P.J.); (N.P.L.); (N.K.P.); (Y.-S.C.)
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan 47392, Korea
| | - Nguyen Phuoc Long
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Korea; (R.P.J.); (N.P.L.); (N.K.P.); (Y.-S.C.)
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan 47392, Korea
| | - Nguyen Ky Phat
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Korea; (R.P.J.); (N.P.L.); (N.K.P.); (Y.-S.C.)
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan 47392, Korea
| | - Yong-Soon Cho
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Korea; (R.P.J.); (N.P.L.); (N.K.P.); (Y.-S.C.)
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan 47392, Korea
| | - Jae-Gook Shin
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Korea; (R.P.J.); (N.P.L.); (N.K.P.); (Y.-S.C.)
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan 47392, Korea
- Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan 47392, Korea
- Correspondence: ; Tel.: +82-51-890-6709; Fax: +82-51-893-1232
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12
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Daali Y, Rostami-Hodjegan A, Samer CF. Editorial: Precision Medicine: Impact of Cytochromes P450 and Transporters Genetic Polymorphisms, Drug-Drug Interactions, Disease on Safety and Efficacy of Drugs. Front Pharmacol 2022; 12:834717. [PMID: 35115950 PMCID: PMC8804351 DOI: 10.3389/fphar.2021.834717] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 12/24/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Youssef Daali
- Division of Clinical Pharmacology and Toxicology, Department of Anesthesiology, Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, Geneva, Switzerland.,Laboratory of Clinical Pharmacology, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester, United Kingdom.,Certara, Princeton, NJ, United States
| | - Caroline F Samer
- Division of Clinical Pharmacology and Toxicology, Department of Anesthesiology, Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland.,Laboratory of Clinical Pharmacology, Faculty of Medicine, University of Geneva, Geneva, Switzerland
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13
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Achour B, Al‐Majdoub ZM, Grybos‐Gajniak A, Lea K, Kilford P, Zhang M, Knight D, Barber J, Schageman J, Rostami‐Hodjegan A. Liquid Biopsy Enables Quantification of the Abundance and Interindividual Variability of Hepatic Enzymes and Transporters. Clin Pharmacol Ther 2021; 109:222-232. [PMID: 33141922 PMCID: PMC7839483 DOI: 10.1002/cpt.2102] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 10/14/2020] [Indexed: 12/31/2022]
Abstract
Variability in individual capacity for hepatic elimination of therapeutic drugs is well recognized and is associated with variable expression and activity of liver enzymes and transporters. Although genotyping offers some degree of stratification, there is often large variability within the same genotype. Direct measurement of protein expression is impractical due to limited access to tissue biopsies. Hence, determination of variability in hepatic drug metabolism and disposition using liquid biopsy (blood samples) is an attractive proposition during drug development and in clinical practice. This study used a multi-"omic" strategy to establish a liquid biopsy technology intended to assess hepatic capacity for metabolism and disposition in individual patients. Plasma exosomal analysis (n = 29) revealed expression of 533 pharmacologically relevant genes at the RNA level, with 147 genes showing evidence of expression at the protein level in matching liver tissue. Correction of exosomal RNA expression using a novel shedding factor improved correlation against liver protein expression for 97 liver-enriched genes. Strong correlation was demonstrated for 12 key drug-metabolizing enzymes and 4 drug transporters. The developed test allowed reliable patient stratification, and in silico trials demonstrated utility in adjusting drug dose to achieve similar drug exposure between patients with variable hepatic elimination. Accordingly, this approach can be applied in characterization of volunteers prior to enrollment in clinical trials and for patient stratification in clinical practice to achieve more precise individual dosing.
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Affiliation(s)
- Brahim Achour
- Centre for Applied Pharmacokinetic Research, School of Health SciencesUniversity of ManchesterManchesterUK
| | - Zubida M. Al‐Majdoub
- Centre for Applied Pharmacokinetic Research, School of Health SciencesUniversity of ManchesterManchesterUK
| | | | | | | | | | - David Knight
- Biological Mass Spectrometry Core FacilityUniversity of ManchesterManchesterUK
| | - Jill Barber
- Centre for Applied Pharmacokinetic Research, School of Health SciencesUniversity of ManchesterManchesterUK
| | | | - Amin Rostami‐Hodjegan
- Centre for Applied Pharmacokinetic Research, School of Health SciencesUniversity of ManchesterManchesterUK
- Certara Ltd.PrincetonNew JerseyUSA
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14
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Uster DW, Stocker SL, Carland JE, Brett J, Marriott DJE, Day RO, Wicha SG. A Model Averaging/Selection Approach Improves the Predictive Performance of Model-Informed Precision Dosing: Vancomycin as a Case Study. Clin Pharmacol Ther 2020; 109:175-183. [PMID: 32996120 DOI: 10.1002/cpt.2065] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 09/12/2020] [Indexed: 11/10/2022]
Abstract
Many important drugs exhibit substantial variability in pharmacokinetics and pharmacodynamics leading to a loss of the desired clinical outcomes or significant adverse effects. Forecasting drug exposures using pharmacometric models can improve individual target attainment when compared with conventional therapeutic drug monitoring (TDM). However, selecting the "correct" model for this model-informed precision dosing (MIPD) is challenging. We derived and evaluated a model selection algorithm (MSA) and a model averaging algorithm (MAA), which automates model selection and finds the best model or combination of models for each patient using vancomycin as a case study, and implemented both algorithms in the MIPD software "TDMx." The predictive performance (based on accuracy and precision) of the two algorithms was assessed in (i) a simulation study of six distinct populations and (ii) a clinical dataset of 180 patients undergoing TDM during vancomycin treatment and compared with the performance obtained using a single model. Throughout the six virtual populations the MSA and MAA (imprecision: 9.9-24.2%, inaccuracy: less than ± 8.2%) displayed more accurate predictions than the single models (imprecision: 8.9-51.1%; inaccuracy: up to 28.9%). In the clinical dataset, the predictive performance of the single models applying at least one plasma concentration varied substantially (imprecision: 28-62%, inaccuracy: -16 to 25%), whereas the MSA or MAA utilizing these models simultaneously resulted in unbiased and precise predictions (imprecision: 29% and 30%, inaccuracy: -5% and 0%, respectively). MSA and MAA approaches implemented in TDMx might thereby lower the burden of fit-for-purpose validation of individual models and streamline MIPD.
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Affiliation(s)
- David W Uster
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Hamburg, Germany
| | - Sophie L Stocker
- Department of Clinical Pharmacology and Toxicology, St. Vincent's Hospital, Sydney, New South Wales, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Jane E Carland
- Department of Clinical Pharmacology and Toxicology, St. Vincent's Hospital, Sydney, New South Wales, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Jonathan Brett
- Department of Clinical Pharmacology and Toxicology, St. Vincent's Hospital, Sydney, New South Wales, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Deborah J E Marriott
- St. Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia.,Department of Clinical Microbiology and Infectious Diseases, St. Vincent's Hospital, Sydney, New South Wales, Australia
| | - Richard O Day
- Department of Clinical Pharmacology and Toxicology, St. Vincent's Hospital, Sydney, New South Wales, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Sebastian G Wicha
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Hamburg, Germany
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15
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Darwich AS, Polasek TM, Aronson JK, Ogungbenro K, Wright DFB, Achour B, Reny JL, Daali Y, Eiermann B, Cook J, Lesko L, McLachlan AJ, Rostami-Hodjegan A. Model-Informed Precision Dosing: Background, Requirements, Validation, Implementation, and Forward Trajectory of Individualizing Drug Therapy. Annu Rev Pharmacol Toxicol 2020; 61:225-245. [PMID: 33035445 DOI: 10.1146/annurev-pharmtox-033020-113257] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Model-informed precision dosing (MIPD) has become synonymous with modern approaches for individualizing drug therapy, in which the characteristics of each patient are considered as opposed to applying a one-size-fits-all alternative. This review provides a brief account of the current knowledge, practices, and opinions on MIPD while defining an achievable vision for MIPD in clinical care based on available evidence. We begin with a historical perspective on variability in dose requirements and then discuss technical aspects of MIPD, including the need for clinical decision support tools, practical validation, and implementation of MIPD in health care. We also discuss novel ways to characterize patient variability beyond the common perceptions of genetic control. Finally, we address current debates on MIPD from the perspectives of the new drug development, health economics, and drug regulations.
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Affiliation(s)
- Adam S Darwich
- Logistics and Informatics in Health Care, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), KTH Royal Institute of Technology, SE-141 57 Huddinge, Sweden
| | - Thomas M Polasek
- Department of Clinical Pharmacology, Royal Adelaide Hospital, Adelaide, South Australia 5000, Australia.,Centre for Medicine Use and Safety, Monash University, Melbourne, Victoria 3052, Australia.,Certara, Princeton, New Jersey 08540, USA
| | - Jeffrey K Aronson
- Centre for Evidence Based Medicine, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, United Kingdom
| | - Kayode Ogungbenro
- Centre for Applied Pharmacokinetic Research, The University of Manchester, Manchester M13 9PT, United Kingdom;
| | | | - Brahim Achour
- Centre for Applied Pharmacokinetic Research, The University of Manchester, Manchester M13 9PT, United Kingdom;
| | - Jean-Luc Reny
- Geneva Platelet Group, Faculty of Medicine, University of Geneva, CH-1211 Geneva, Switzerland.,Division of General Internal Medicine, Geneva University Hospitals, CH-1211 Geneva, Switzerland
| | - Youssef Daali
- Geneva Platelet Group, Faculty of Medicine, University of Geneva, CH-1211 Geneva, Switzerland
| | - Birgit Eiermann
- Inera AB, Swedish Association of Local Authorities and Regions, SE-118 93 Stockholm, Sweden
| | - Jack Cook
- Drug Safety Research & Development, Pfizer Inc., Groton, Connecticut 06340, USA
| | - Lawrence Lesko
- Center for Pharmacometrics and Systems Pharmacology, University of Florida, Orlando, Florida 32827, USA
| | - Andrew J McLachlan
- School of Pharmacy, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Amin Rostami-Hodjegan
- Certara, Princeton, New Jersey 08540, USA.,Centre for Applied Pharmacokinetic Research, The University of Manchester, Manchester M13 9PT, United Kingdom;
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16
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Kim SW, Kim DJ, Zang DY, Lee DH. Impact of Sampling Period on Population Pharmacokinetic Analysis of Antibiotics: Why do You Take Blood Samples Following the Fourth Dose? Pharmaceuticals (Basel) 2020; 13:ph13090249. [PMID: 32947890 PMCID: PMC7558941 DOI: 10.3390/ph13090249] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 09/06/2020] [Accepted: 09/14/2020] [Indexed: 12/15/2022] Open
Abstract
To date, many population pharmacokinetic models of antibiotics have been developed using blood sampling data after the fourth or fifth dose, which represents steady-state levels. However, if a model developed using blood sampled after the first dose is equivalent to that using blood sampled after the fourth dose, it would be advantageous to utilize the former. The aim of this study was to investigate the effect of blood sampling after the first and/or fourth drug administration on the accuracy and precision of parameter estimates. A previously reported robust, two-compartment model of vancomycin was used for simulation to evaluate the performances of the parameter estimates. The parameter estimation performances were assessed using relative bias and relative root mean square error. Performance was investigated in 72 scenarios consisting of a combination of two blood sampling periods (the first and fourth dose), two total clearances, three infusion times, and four sample sizes. The population pharmacokinetic models from data collected at the first dose and those collected at the fourth dose produced parameter estimates that were similar in accuracy and precision. This study will contribute to increasing the efficiency and simplicity of antibiotic pharmacokinetic studies.
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Affiliation(s)
- So Won Kim
- Department of Pharmacology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea;
| | - Dong Jin Kim
- Drug Evaluation Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Osong, Cheongju 28159, Korea;
| | - Dae Young Zang
- Division of Hematology-Oncology, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14066, Korea;
| | - Dong-Hwan Lee
- Department of Clinical Pharmacology, Hallym University Sacred Heart Hospital, Anyang 14066, Korea
- Correspondence: ; Tel.: +82-31-380-4778
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17
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Alsultan A, Alghamdi WA, Alghamdi J, Alharbi AF, Aljutayli A, Albassam A, Almazroo O, Alqahtani S. Clinical pharmacology applications in clinical drug development and clinical care: A focus on Saudi Arabia. Saudi Pharm J 2020; 28:1217-1227. [PMID: 33132716 PMCID: PMC7584801 DOI: 10.1016/j.jsps.2020.08.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Accepted: 08/14/2020] [Indexed: 01/10/2023] Open
Abstract
Drug development, from preclinical to clinical studies, is a lengthy and complex process. There is an increased interest in the Kingdom of Saudi Arabia (KSA) to promote innovation, research and local content including clinical trials (Phase I-IV). Currently, there are over 650 registered clinical trials in Saudi Arabia, and this number is expected to increase. An important part of drug development and clinical trials is to assure the safe and effective use of drugs. Clinical pharmacology plays a vital role in informed decision making during the drug development stage as it focuses on the effects of drugs in humans. Disciplines such as pharmacokinetics, pharmacodynamics and pharmacogenomics are components of clinical pharmacology. It is a growing discipline with a range of applications in all phases of drug development, including selecting optimal doses for Phase I, II and III studies, evaluating bioequivalence and biosimilar studies and designing clinical studies. Incorporating clinical pharmacology in research as well as in the requirements of regulatory agencies will improve the drug development process and accelerate the pipeline. Clinical pharmacology is also applied in direct patient care with the goal of personalizing treatment. Tools such as therapeutic drug monitoring, pharmacogenomics and model informed precision dosing are used to optimize dosing for patients at an individual level. In KSA, the science of clinical pharmacology is underutilized and we believe it is important to raise awareness and educate the scientific community and healthcare professionals in terms of its applications and potential. In this review paper, we provide an overview on the use and applications of clinical pharmacology in both drug development and clinical care.
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Affiliation(s)
- Abdullah Alsultan
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.,Clinical Pharmacokinetics and Pharmacodynamics Unit, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Wael A Alghamdi
- Department of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha, Saudi Arabia
| | - Jahad Alghamdi
- The Saudi Biobank, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Abeer F Alharbi
- College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
| | | | - Ahmed Albassam
- Department of Clinical Pharmacy, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | | | - Saeed Alqahtani
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.,Clinical Pharmacokinetics and Pharmacodynamics Unit, King Saud University Medical City, Riyadh, Saudi Arabia
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18
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Dreesen E, Berends S, Laharie D, D'Haens G, Vermeire S, Gils A, Mathôt R. Modelling of the relationship between infliximab exposure, faecal calprotectin and endoscopic remission in patients with Crohn's disease. Br J Clin Pharmacol 2020; 87:106-118. [PMID: 32415677 DOI: 10.1111/bcp.14364] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 05/05/2020] [Accepted: 05/07/2020] [Indexed: 12/22/2022] Open
Abstract
AIMS Evidence for the benefits of pharmacokinetic (PK) and pharmacodynamic (PD) monitoring of infliximab in patients with Crohn's disease (CD) remains scarce. We aimed to develop a population (pop)PK/PD model to characterise the infliximab dose-exposure-biomarker-response (faecal calprotectin [fCal] and endoscopic remission [ER]) relationship. METHODS Data were obtained from 116 patients with CD in a phase 4 dose-escalation study. Three sequential models were developed: a 2-compartment popPK model linking infliximab dose to exposure; an indirect response popPK/PD model describing the inhibitory effect of infliximab exposure on fCal; and a first-order Markov popPD model linking fCal to transitions between states of ER, no ER and dropout. RESULTS Infliximab clearance increased with increasing fCal, decreasing albumin, increasing CD activity index and presence of anti-drug antibodies. Baseline fCal increased with increasing C-reactive protein and decreasing platelet count. Lower fCal increased the probability of attaining ER and decreased the probability of losing ER. Probability of dropping out given an earlier state of absence of ER increased with time. Large interpatient PK and PD variability resulted in a flat dose-response curve. Predicted fraction of patients achieving ER was 45% [30-61] (median [interquartile range], n = 50 000) when on 5 mg/kg infliximab (~46% observed in data). Simulations with 10 mg/kg induction doses predicted an increase to 48% [32-63]. This minor benefit at the population level argues against systematic 10 mg/kg induction dosing in all patients. CONCLUSION Model-informed infliximab dose optimisation towards a predefined fCal concentration (while accounting for PK and PD variability) may improve effectiveness of infliximab therapy.
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Affiliation(s)
- Erwin Dreesen
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
| | - Sophie Berends
- Hospital Pharmacy, Amsterdam UMC, Amsterdam, Netherlands
| | - David Laharie
- Department of Hepato-gastroenterology and Digestive Oncology, Haut-Lévêque Hospital, Bordeaux UMC, Bordeaux, France
| | - Geert D'Haens
- Department of Gastroenterology and Hepatology, Amsterdam UMC, Amsterdam, Netherlands
| | - Séverine Vermeire
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium
| | - Ann Gils
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
| | - Ron Mathôt
- Hospital Pharmacy, Amsterdam UMC, Amsterdam, Netherlands
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19
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Kantasiripitak W, Van Daele R, Gijsen M, Ferrante M, Spriet I, Dreesen E. Software Tools for Model-Informed Precision Dosing: How Well Do They Satisfy the Needs? Front Pharmacol 2020; 11:620. [PMID: 32457619 PMCID: PMC7224248 DOI: 10.3389/fphar.2020.00620] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 04/20/2020] [Indexed: 12/11/2022] Open
Abstract
Model-informed precision dosing (MIPD) software tools are used to optimize dosage regimens in individual patients, aiming to achieve drug exposure targets associated with desirable clinical outcomes. Over the last few decades, numerous MIPD software tools have been developed. However, they have still not been widely integrated into clinical practice. This study focuses on identifying the requirements for and evaluating the performance of the currently available MIPD software tools. First, a total of 22 experts in the field of precision dosing completed a web survey to assess the importance (from 0; do not agree at all, to 10; completely agree) of 103 pre-established software tool criteria organized in eight categories: user-friendliness and utilization, user support, computational aspects, population models, quality and validation, output generation, privacy and data security, and cost. Category mean ± pooled standard deviation importance scores ranged from 7.2 ± 2.1 (user-friendliness and utilization) to 8.5 ± 1.8 (privacy and data security). The relative importance score of each criterion within a category was used as a weighting factor in the subsequent evaluation of the software tools. Ten software tools were identified through literature and internet searches: four software tools were provided by companies (DoseMeRx, InsightRX Nova, MwPharm++, and PrecisePK) and six were provided by non-company owners (AutoKinetics, BestDose, ID-ODS, NextDose, TDMx, and Tucuxi). All software tools performed well in all categories, although there were differences in terms of in-built software features, user interface design, the number of drug modules and populations, user support, quality control, and cost. Therefore, the choice for a certain software tool should be made based on these differences and personal preferences. However, there are still improvements to be made in terms of electronic health record integration, standardization of software and model validation strategies, and prospective evidence for the software tools’ clinical and cost benefits.
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Affiliation(s)
- Wannee Kantasiripitak
- Therapeutic and Diagnostic Antibodies Unit, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
| | - Ruth Van Daele
- Clinical Pharmacology and Pharmacotherapy Unit, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.,Pharmacy Department, University Hospitals Leuven, Leuven, Belgium
| | - Matthias Gijsen
- Clinical Pharmacology and Pharmacotherapy Unit, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.,Pharmacy Department, University Hospitals Leuven, Leuven, Belgium
| | - Marc Ferrante
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium.,Translational Research Center for Gastrointestinal Disorders, Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | - Isabel Spriet
- Clinical Pharmacology and Pharmacotherapy Unit, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.,Pharmacy Department, University Hospitals Leuven, Leuven, Belgium
| | - Erwin Dreesen
- Therapeutic and Diagnostic Antibodies Unit, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.,Clinical Pharmacology and Pharmacotherapy Unit, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
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20
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Tyson RJ, Park CC, Powell JR, Patterson JH, Weiner D, Watkins PB, Gonzalez D. Precision Dosing Priority Criteria: Drug, Disease, and Patient Population Variables. Front Pharmacol 2020; 11:420. [PMID: 32390828 PMCID: PMC7188913 DOI: 10.3389/fphar.2020.00420] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 03/19/2020] [Indexed: 12/12/2022] Open
Abstract
The administered dose of a drug modulates whether patients will experience optimal effectiveness, toxicity including death, or no effect at all. Dosing is particularly important for diseases and/or drugs where the drug can decrease severe morbidity or prolong life. Likewise, dosing is important where the drug can cause death or severe morbidity. Since we believe there are many examples where more precise dosing could benefit patients, it is worthwhile to consider how to prioritize drug-disease targets. One key consideration is the quality of information available from which more precise dosing recommendations can be constructed. When a new more precise dosing scheme is created and differs significantly from the approved label, it is important to consider the level of proof necessary to either change the label and/or change clinical practice. The cost and effort needed to provide this proof should also be considered in prioritizing drug-disease precision dosing targets. Although precision dosing is being promoted and has great promise, it is underutilized in many drugs and disease states. Therefore, we believe it is important to consider how more precise dosing is going to be delivered to high priority patients in a timely manner. If better dosing schemes do not change clinical practice resulting in better patient outcomes, then what is the use? This review paper discusses variables to consider when prioritizing precision dosing candidates while highlighting key examples of precision dosing that have been successfully used to improve patient care.
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Affiliation(s)
- Rachel J. Tyson
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Christine C. Park
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - J. Robert Powell
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - J. Herbert Patterson
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Daniel Weiner
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Paul B. Watkins
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Institute for Drug Safety Sciences, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Daniel Gonzalez
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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21
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Abstract
In the last few years, single-cell profiling of taste cells and ganglion cells has advanced our understanding of transduction, encoding, and transmission of information from taste buds as relayed to the central nervous system. This review focuses on new knowledge from these molecular approaches and attempts to place this in the context of previous questions and findings in the field. The individual taste cells within a taste bud are molecularly specialized for detection of one of the primary taste qualities: salt, sour, sweet, umami, and bitter. Transduction and transmitter release mechanisms differ substantially for taste cells transducing sour (Type III cells) compared with those transducing the qualities of sweet, umami, or bitter (Type II cells), although ultimately all transmission of taste relies on activation of purinergic P2X receptors on the afferent nerves. The ganglion cells providing innervation to the taste buds also appear divisible into functional and molecular subtypes, and each ganglion cell is primarily but not exclusively responsive to one taste quality.
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Affiliation(s)
- Sue C. Kinnamon
- Rocky Mountain Taste & Smell Center, Department of Otolaryngology and Department of Cell & Developmental Biology, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Thomas E. Finger
- Rocky Mountain Taste & Smell Center, Department of Otolaryngology and Department of Cell & Developmental Biology, University of Colorado School of Medicine, Aurora, CO, 80045, USA
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22
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Polasek TM, Kirkpatrick CMJ, Rostami-Hodjegan A. Precision dosing to avoid adverse drug reactions. Ther Adv Drug Saf 2019; 10:2042098619894147. [PMID: 31853362 PMCID: PMC6909265 DOI: 10.1177/2042098619894147] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 11/13/2019] [Indexed: 12/15/2022] Open
Abstract
Adverse drug reactions (ADRs) have traditionally been managed by trial and error, adjusting drug and dose selection reactively following patient harm. With an improved understanding of ADRs, and the patient characteristics that increase susceptibility, precision medicine technologies enable a proactive approach to ADRs and support clinicians to change prescribing accordingly. This commentary revisits the famous pharmacology–toxicology continuum first postulated by Paracelsus 500 years ago and explains why precision dosing is needed to help avoid ADRs in modern clinical practice. Strategies on how to improve precision dosing are given, including more research to establish better precision dosing targets in the cases of greatest need, easier access to dosing instructions via e-prescribing, improved monitoring of patients with novel biomarkers of drug response, and further application of model-informed precision dosing.
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Affiliation(s)
- Thomas M Polasek
- Certara, 100 Overlook Center, Suite 101, Princeton, NJ 08540 USA
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23
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Polasek TM, Shakib S, Rostami-Hodjegan A. Precision medicine technology hype or reality? The example of computer-guided dosing. F1000Res 2019; 8:1709. [PMID: 31754426 PMCID: PMC6852323 DOI: 10.12688/f1000research.20489.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/25/2019] [Indexed: 12/19/2022] Open
Abstract
Novel technologies labelled as ‘precision medicine’ are targeting all aspects of clinical care. Whilst some technological advances are undeniably exciting, many doctors at the frontline of healthcare view precision medicine as being out of reach for their patients. Computer-guided dosing is a precision medicine technology that predicts drug concentrations and drug responses based on individual patient characteristics. In this opinion piece, the example of computer-guided dosing is used to illustrate eight features of a precision medicine technology less likely to be hyperbole and more likely to improve patient care. Positive features in this regard include: (1) fitting the definition of ‘precision medicine’; (2) addressing a major clinical problem that negatively impacts patient care; (3) a track record of high-quality medical science published via peer-reviewed literature; (4) well-defined clinical cases for application; (5) quality evidence of benefits measured by various clinical, patient and health economic endpoints; (6) strong economic drivers; (7) user friendliness, including easy integration into clinical workflow, and (8) recognition of importance by patients and their endorsement for broader clinical use. Barriers raised by critics of the approach are given to balance the view. The value of computer-guided dosing will be decided ultimately by the extent to which it can improve cost-effective patient care.
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Affiliation(s)
- Thomas M Polasek
- Certara, 100 Overlook Center, Suite 101, Princeton, NJ, 08540, USA.,Centre for Medicines Use and Safety, Monash University, Melbourne, Victoria, Australia.,Department of Clinical Pharmacology, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Sepehr Shakib
- Department of Clinical Pharmacology, Royal Adelaide Hospital, Adelaide, South Australia, Australia.,Discipline of Pharmacology, Adelaide Medical School, University of Adelaide, Adelaide, Australia
| | - Amin Rostami-Hodjegan
- Certara, 100 Overlook Center, Suite 101, Princeton, NJ, 08540, USA.,Centre for Applied Pharmacokinetic Research, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
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24
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Wright DFB, Martin JH, Cremers S. Spotlight Commentary: Model-informed precision dosing must demonstrate improved patient outcomes. Br J Clin Pharmacol 2019; 85:2238-2240. [PMID: 31400011 DOI: 10.1111/bcp.14050] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 06/20/2019] [Accepted: 06/21/2019] [Indexed: 12/31/2022] Open
Affiliation(s)
| | - Jennifer H Martin
- School of Medicine and Public Health, University of Newcastle, Newcastle, Australia
| | - Serge Cremers
- Departments of Pathology and Cell Biology, and Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA
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