ArticleBritish journal of clinical pharmacology2017
A physiologically based pharmacokinetic modelling approach to predict buprenorphine pharmacokinetics following intravenous and sublingual administration.
Article in British journal of clinical pharmacology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 28 citations in OpenAlex.
- Buprenorphine to reverse respiratory depression from methadone overdose in opioid-dependent patients: a prospective randomized trial.Critical care (London, England) · 2020Trial
- Article
- Pharmacometric modeling of opioid disposition in pregnancy: a systematic review of PopPK and PBPK approaches, model limitations, and future directions.Frontiers in pharmacology · 2026Review
- Development and Verification of a Full Physiologically Based Pharmacokinetic Model for Sublingual Buprenorphine in Healthy Adult Volunteers that Accounts for Nonlinear Bioavailability.Drug metabolism and disposition: the biological fate of chemicals · 2024Article
- Long-Acting Opioid Analgesics for Acute Pain: Pharmacokinetic Evidence Reviewed.Veterinary sciences · 2023Review
- Review
- Physiologically-Based Pharmacokinetic Modeling to Investigate the Effect of Maturation on Buprenorphine Pharmacokinetics in Newborns with Neonatal Opioid Withdrawal Syndrome.Clinical pharmacology and therapeutics · 2022Article
- Physiologically based pharmacokinetic modelling in pregnancy: Model reproducibility and external validation.British journal of clinical pharmacology · 2022Article
- Physiologically based pharmacokinetic modelling of treprostinil after intravenous injection and extended-release oral tablet administration in healthy volunteers: An extrapolation to other patient populations including patients with hepatic impairment.British journal of clinical pharmacology · 2022Article
- In Silico Simulation of the Systemic Drug Exposure Following the Topical Application of Opioid Analgesics in Patients with Cutaneous Lesions.Pharmaceutics · 2021Article
- Pharmacokinetic Characterization and External Evaluation of a Quantitative Framework of Sublingual Buprenorphine in Patients with an Opioid Disorder in Puerto Rico.Pharmaceutics · 2020Article
- Physiologically-Based Pharmacokinetic (PBPK) Modeling of Buprenorphine in Adults, Children and Preterm Neonates.Pharmaceutics · 2020Article
- Scalable in silico Simulation of Transdermal Drug Permeability: Application of BIOiSIM Platform.Drug design, development and therapy · 2020Article
- Utilization of Physiologically Based Pharmacokinetic Modeling in Clinical Pharmacology and Therapeutics: an Overview.Current pharmacology reports · 2020Review
- Computational framework for predictive PBPK-PD-Tox simulations of opioids and antidotes.Journal of pharmacokinetics and pharmacodynamics · 2019Article
- Prediction of Drug-Drug Interactions Between Opioids and Overdosed Benzodiazepines Using Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation.Drugs in R&D · 2019Article
- Physiologically Based Pharmacokinetic Modeling to Understand the Absorption of Risperidone Orodispersible Film.Frontiers in pharmacology · 2019Article
- Gestational changes in buprenorphine exposure: A physiologically-based pharmacokinetic analysis.British journal of clinical pharmacology · 2018Article
- A physiologically based pharmacokinetic modelling approach to predict buprenorphine pharmacokinetics following intravenous and sublingual administration.British journal of clinical pharmacology · 2017Article
- An evidence-based recommendation to increase the dosing frequency of buprenorphine during pregnancy.American journal of obstetrics and gynecology · 2017Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
aimsOpioid dependence is associated with high morbidity and mortality. Buprenorphine (BUP) is approved by the Food and Drug Administration to treat opioid dependence. There is a lack of clear consensus on the appropriate dosing of BUP due to interpatient physiological differences in absorption/disposition, subjective response assessment and other patient comorbidities. The objective of the present study was to build and validate robust physiologically based pharmacokinetic (PBPK) models for intravenous (IV) and sublingual (SL) BUP as a first step to optimizing BUP pharmacotherapy.
methodsBUP-PBPK modelling and simulations were performed using SimCyp® by incorporating the physiochemical properties of BUP, establishing intersystem extrapolation factors-based in vitro-in-vivo extrapolation (IVIVE) methods to extrapolate in vitro enzyme activity data, and using tissue-specific plasma partition coefficient estimations. Published data on IV and SL-BUP in opioid-dependent and non-opioid-dependent patients were used to build the models. Fourteen model-naïve BUP-PK datasets were used for inter- and intrastudy validations.
resultsThe IV and SL-BUP-PBPK models developed were robust in predicting the multicompartment disposition of BUP over a dosing range of 0.3-32 mg. Predicted plasma concentration-time profiles in virtual patients were consistent with reported data across five single-dose IV, five single-dose SL and four multiple dose SL studies. All PK parameter predictions were within 75-137% of the corresponding observed data. The model developed predicted the brain exposure of BUP to be about four times higher than that of BUP in plasma.
conclusionThe validated PBPK models will be used in future studies to predict BUP plasma and brain concentrations based on the varying demographic, physiological and pathological characteristics of patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.