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Peng H, Han B, Tong T, Jin X, Peng Y, Guo M, Li B, Ding J, Kong Q, Wang Q. 3D printing processes in precise drug delivery for personalized medicine. Biofabrication 2024; 16:10.1088/1758-5090/ad3a14. [PMID: 38569493 PMCID: PMC11164598 DOI: 10.1088/1758-5090/ad3a14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 04/03/2024] [Indexed: 04/05/2024]
Abstract
With the advent of personalized medicine, the drug delivery system will be changed significantly. The development of personalized medicine needs the support of many technologies, among which three-dimensional printing (3DP) technology is a novel formulation-preparing process that creates 3D objects by depositing printing materials layer-by-layer based on the computer-aided design method. Compared with traditional pharmaceutical processes, 3DP produces complex drug combinations, personalized dosage, and flexible shape and structure of dosage forms (DFs) on demand. In the future, personalized 3DP drugs may supplement and even replace their traditional counterpart. We systematically introduce the applications of 3DP technologies in the pharmaceutical industry and summarize the virtues and shortcomings of each technique. The release behaviors and control mechanisms of the pharmaceutical DFs with desired structures are also analyzed. Finally, the benefits, challenges, and prospects of 3DP technology to the pharmaceutical industry are discussed.
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Affiliation(s)
- Haisheng Peng
- Department of Pharmacology, Medical College, University of Shaoxing, Shaoxing, People’s Republic of China
- These authors contributed equally
| | - Bo Han
- Department of Pharmacy, Daqing Branch, Harbin Medical University, Daqing, People’s Republic of China
- These authors contributed equally
| | - Tianjian Tong
- Department of Chemical and Biological Engineering, Iowa State University, Ames, IA 50011, United States of America
| | - Xin Jin
- Department of Pharmacology, Medical College, University of Shaoxing, Shaoxing, People’s Republic of China
| | - Yanbo Peng
- Department of Pharmaceutical Engineering, China Pharmaceutical University, 639 Longmian Rd, Nanjing 211198, People’s Republic of China
| | - Meitong Guo
- Department of Pharmacology, Medical College, University of Shaoxing, Shaoxing, People’s Republic of China
| | - Bian Li
- Department of Pharmacology, Medical College, University of Shaoxing, Shaoxing, People’s Republic of China
| | - Jiaxin Ding
- Department of Pharmacology, Medical College, University of Shaoxing, Shaoxing, People’s Republic of China
| | - Qingfei Kong
- Department of Neurobiology, Harbin Medical University, Heilongjiang Provincial Key Laboratory of Neurobiology, Harbin, Heilongjiang 150086, People’s Republic of China
| | - Qun Wang
- Department of Chemical and Biological Engineering, Iowa State University, Ames, IA 50011, United States of America
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Zheng L, Zhang W, Olkkola KT, Dallmann A, Ni L, Zhao Y, Wang L, Zhang Q, Hu W. Physiologically based pharmacokinetic modeling of ritonavir-oxycodone drug interactions and its implication for dosing strategy. Eur J Pharm Sci 2024; 194:106697. [PMID: 38199444 DOI: 10.1016/j.ejps.2024.106697] [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: 07/03/2023] [Revised: 11/13/2023] [Accepted: 01/07/2024] [Indexed: 01/12/2024]
Abstract
The concomitant administration of ritonavir and oxycodone may significantly increase the plasma concentrations of oxycodone. This study was aimed to simulate DDI between ritonavir and oxycodone, a widely used opioid, and to formulate dosing protocols for oxycodone by using physiologically based pharmacokinetic (PBPK) modeling. We developed a ritonavir PBPK model incorporating induction and competitive and time-dependent inhibition of CYP3A4 and competitive inhibition of CYP2D6. The ritonavir model was evaluated with observed clinical pharmacokinetic data and validated for its CYP3A4 inhibition potency. We then used the model to simulate drug interactions between oxycodone and ritonavir under various dosing scenarios. The developed model captured the pharmacokinetic characteristics of ritonavir from clinical studies. The model also accurately predicts exposure changes of midazolam, triazolam, and oxycodone in the presence of ritonavir. According to model simulations, the steady-state maximum, minimum and average concentrations of oxycodone increased by up to 166% after co-administration with ritonavir, and the total exposure increased by approximately 120%. To achieve similar steady-state concentrations, halving the dose with an unchanged dosing interval or doubling the dosing interval with an unaltered single dose should be practical for oxycodone, whether formulated in uncoated or controlled-release tablets during long-term co-medication with ritonavir. The results revealed exposure-related risks of oxycodone-ritonavir interactions that have not been studied clinically and emphasized PBPK as a workable method to direct judicious dosage.
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Affiliation(s)
- Liang Zheng
- Department of Clinical Pharmacology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
| | - Wei Zhang
- Department of Clinical Pharmacology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Klaus T Olkkola
- Department of Anaesthesiology and Intensive Care Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
| | - André Dallmann
- Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany.
| | - Liang Ni
- Clinical Pharmacokinetics Laboratory, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Yingjie Zhao
- Department of Clinical Pharmacology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Ling Wang
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - Qian Zhang
- Department of Clinical Pharmacology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
| | - Wei Hu
- Department of Clinical Pharmacology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
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Eapen-John D, Mohiuddin AG, Kennedy JL. A potential paradigm shift in opioid crisis management: The role of pharmacogenomics. World J Biol Psychiatry 2022; 23:411-423. [PMID: 34854362 DOI: 10.1080/15622975.2021.2012397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Pharmacogenetic investigations into the opioid crisis suggest genetic variation could be a significant cause of opioid-related morbidity and mortality. Variability in opioid system genes, including single nucleotide polymorphisms, manifest after pharmacogenetic testing, as previously invisible risk factors for addiction and overdose. Pharmacodynamic genes regulate opioid-sensitive brain networks and neural reward circuitry. Pharmacokinetic genes expressed in drug metabolic pathways regulate blood levels of active vs. inactive opioid metabolites. Elucidating the complex interplay of genetic variations in pharmacokinetic and pharmacodynamic pathways will shed new light on the addictive and toxic properties of opioids. This narrative review serves to promote understanding of key genetic mechanisms affecting the metabolism and actions of opioids, and to explore causes of the recent surge in opioid-related mortality associated with COVID-19. Personalised treatment plans centred around an individual's genetic makeup could make opioid-based pain management and opioid use disorder (OUD) treatments safer and more effective at both the individual and system levels.
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Affiliation(s)
- David Eapen-John
- Molecular Brain Science, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada.,Institute of Medical Science, University of Toronto, Toronto, Canada
| | - Ayeshah G Mohiuddin
- Molecular Brain Science, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada.,Institute of Medical Science, University of Toronto, Toronto, Canada
| | - James L Kennedy
- Molecular Brain Science, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada.,Institute of Medical Science, University of Toronto, Toronto, Canada.,Department of Psychiatry, University of Toronto, Toronto, Canada
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Designing and conducting proof-of-concept chronic pain analgesic clinical trials. Pain Rep 2019; 4:e697. [PMID: 31583338 PMCID: PMC6749910 DOI: 10.1097/pr9.0000000000000697] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 09/24/2018] [Accepted: 09/26/2018] [Indexed: 02/07/2023] Open
Abstract
Introduction: The evolution of pain treatment is dependent on successful development and testing of interventions. Proof-of-concept (POC) studies bridge the gap between identification of a novel target and evaluation of the candidate intervention's efficacy within a pain model or the intended clinical pain population. Methods: This narrative review describes and evaluates clinical trial phases, specific POC pain trials, and approaches to patient profiling. Results: We describe common POC trial designs and their value and challenges, a mechanism-based approach, and statistical issues for consideration. Conclusion: Proof-of-concept trials provide initial evidence for target use in a specific population, the most appropriate dosing strategy, and duration of treatment. A significant goal in designing an informative and efficient POC study is to ensure that the study is safe and sufficiently sensitive to detect a preliminary efficacy signal (ie, a potentially valuable therapy). Proof-of-concept studies help avoid resources wasted on targets/molecules that are not likely to succeed. As such, the design of a successful POC trial requires careful consideration of the research objective, patient population, the particular intervention, and outcome(s) of interest. These trials provide the basis for future, larger-scale studies confirming efficacy, tolerability, side effects, and other associated risks.
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Strand MC, Mørland J, Slørdal L, Riedel B, Innerdal C, Aamo T, Mathisrud G, Vindenes V. Conversion factors for assessment of driving impairment after exposure to multiple benzodiazepines/z-hypnotics or opioids. Forensic Sci Int 2017; 281:29-36. [PMID: 29101905 DOI: 10.1016/j.forsciint.2017.10.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2017] [Revised: 09/06/2017] [Accepted: 10/11/2017] [Indexed: 11/18/2022]
Abstract
AIMS Norway has introduced legal concentration limits in blood for 28 non-alcohol drugs in driving under the influence cases. As of 2016 this legislation also regulates the assessment of combined effects of multiple benzodiazepines and opioids. We herein describe the employed methodology for the equivalence tables for concentrations of benzodiazepines/z-hypnotics and opioids implemented in the Norwegian Road Traffic Act. METHODS Legislative limits corresponding to impairment at blood alcohol concentrations (BAC) of 0.02%, 0.05% and 0.12% were established for 15 different benzodiazepines and opioids. This was based on a concept of a linear relationship between blood drug concentration and impairment in drug naïve users. Concentration ratios between these drugs were used to establish conversion factors and calculate net impairment using diazepam and morphine equivalents. RESULTS Conversion factors were established for 14 benzodiazepines/z-hypnotics (alprazolam, bromazepam, clobazam, clonazepam, etizolam, flunitrazepam, lorazepam, nitrazepam, nordiazepam, oxazepam, phenazepam, temazepam, zolpidem and zopiclone) and two opioids (methadone and oxycodone). CONCLUSIONS Conversion factors to calculate diazepam and morphine equivalents for benzodiazepines/z-hypnotics and selected opioids, respectively, have been operative in the Norwegian Road Traffic Act as of February 2016. Calculated equivalents can be applied by the courts to meter out sanctions.
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Affiliation(s)
| | - Jørg Mørland
- Norwegian Institute of Public Health, Division of Health Data and Digitalization, Oslo, Norway.
| | - Lars Slørdal
- Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Trondheim, Norway; Department of Clinical Pharmacology, St. Olav University Hospital, Trondheim, Norway.
| | - Bettina Riedel
- University of Bergen, Faculty of Medicine and Dentistry, Department of Clinical Science, Bergen, Norway; Haukeland University Hospital, Laboratory of Clinical Biochemistry, Bergen, Norway.
| | | | - Trond Aamo
- Department of Clinical Pharmacology, St. Olav University Hospital, Trondheim, Norway.
| | - Grete Mathisrud
- Norwegian Ministry of Transport and Communications, Department of Public Roads and Traffic Safety, Oslo, Norway.
| | - Vigdis Vindenes
- Oslo University Hospital, Department of Forensic Medicine, Oslo, Norway; Center of Drug and Addiction Research, Faculty of Medicine, University of Oslo, Norway.
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Individualized Hydrocodone Therapy Based on Phenotype, Pharmacogenetics, and Pharmacokinetic Dosing. Clin J Pain 2016; 31:1026-35. [PMID: 25621429 DOI: 10.1097/ajp.0000000000000214] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVES (1) To quantify hydrocodone (HC) and hydromorphone (HM) metabolite pharmacokinetics with pharmacogenetics in CYP2D6 ultra-rapid metabolizer (UM), extensive metabolizer (EM), and poor metabolizer (PM) metabolizer phenotypes. (2) To develop an HC phenotype-specific dosing strategy for HC that accounts for HM production using clinical pharmacokinetics integrated with pharmacogenetics for patient safety. SETTING In silico clinical trial simulation. PARTICIPANTS Healthy white men and women without comorbidities or history of opioid, or any other drug or nutraceutical use, age 26.3±5.7 years (mean±SD; range, 19 to 36 y) and weight 71.9±16.8 kg (range, 50 to 108 kg). MAIN OUTCOME MEASURES CYP2D6 phenotype-specific HC clinical pharmacokinetic parameter estimates and phenotype-specific percentages of HM formed from HC. RESULTS PMs had lower indices of HC disposition compared with UMs and EMs. Clearance was reduced by nearly 60% and the t1/2 was increased by about 68% compared with EMs. The canonical order for HC clearance was UM>EM>PM. HC elimination mainly by the liver, represented by ke, was reduced about 70% in PM. However, HC's apparent Vd was not significantly different among UMs, EMs, and PM. The canonical order of predicted plasma HM concentrations was UM>EM>PM. For each of the CYP2D6 phenotypes, the mean predicted HM levels were within HM's therapeutic range, which indicates HC has significant phenotype-dependent pro-drug effects. CONCLUSIONS Our results demonstrate that pharmacogenetics afford clinicians an opportunity to individualize HC dosing, while adding enhanced opportunity to account for its conversion to HM in the body.
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Linares OA, Tod M, Daly AL, Boston RC. Response: Is It Truly the Answer? Personalized Oxycodone Dosing Based on Pharmacogenetic Testing and Corresponding Pharmacokinetics. PAIN MEDICINE 2016; 17:616-619. [DOI: 10.1093/pm/pnv092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Revised: 11/10/2015] [Accepted: 11/29/2015] [Indexed: 11/13/2022]
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