{"title":"The HIV Pharmacology Data Repository (PDR): Setting a new standard for clinical and preclinical pharmacokinetic data sharing","authors":"","doi":"10.1111/bcp.16300","DOIUrl":null,"url":null,"abstract":"<p><b>21</b></p><p><b>The HIV Pharmacology Data Repository (PDR): Setting a new standard for clinical and preclinical pharmacokinetic data sharing</b></p><p>Mackenzie Cottrell<sup>1</sup>, Lauren Tompkins<sup>1</sup>, Adrian Khoei<sup>1</sup>, Alexander Tropsha<sup>1</sup>, Oleg Kapeljushnik<sup>2</sup>, Robert Hubal<sup>2</sup>, Julie Dumond<sup>1</sup> and Angela Kashuba<sup>1</sup></p><p><sup>1</sup><i>UNC Eshelman School of Pharmacy;</i> <sup>2</sup><i>Renaissance Computing Institute at UNC</i></p><p><b>Background:</b> Rapidly expanding clinical pharmacology modelling tools can be used to derive biological meaning through in silico study of archived pharmacokinetic (PK) data pools. Yet, a rate-limiting step to employing these approaches is the ability to access high-quality concentration <i>vs</i>. time (CvT) data, aggregated across disparate study designs in a way that is meaningful and usable for PK modelling. This is partly due to a lack of standardization for PK data description. To this end, we defined and applied a minimum information standard (MIS) for PK data description in the development of a web-based database—the HIV Pharmacology Data Repository (HIV PDR)—and demonstrate scientific utility through population PK modelling.</p><p><b>Materials/methods:</b> We defined the MIS with key reportable variables divided into three categories: intervention (drug, route, time and quantify), system (species dosed and anatomical compartment sampled) and concentration (chemical entity and concentration units quantified, including pro-drugs, drugs and metabolites). We identified 610 archived CvT Excel datasets fulfilling this MIS and created data dictionaries to harmonize terminology. The resulting database is stored in an SQL server with the front-end developed using an ASP.NET core with Angular and the back-end on an SQL Server 2017. We extracted CvT values for tenofovir (TFV) and its active metabolite (tenofovir diphosphate; TFVdp) within human plasma and peripheral blood mononuclear cells (PBMC) from study participants dosed with tenofovir disoproxil fumarate (TDF) and fit a population PK model using NONMEMv7.4.</p><p><b>Results:</b> Our data dictionaries collapsed 924 bioanalytical synonyms (analyte name and units) into 145 unique variables with units parsed in a separate column. Additionally, 246 descriptors of species and anatomical compartment were collapsed into 15 and 80 unique variables, respectively, with taxonomical and anatomical hierarchies. The final database aggregates 80 043 CvT datapoints of 77 chemically distinct compounds. Our extracted TDF dataset contained 913 plasma and 708 PBMC observations from 88 human study participants across three dosing levels (150, 300 and 600 mg) under first-dose and steady-state conditions. The final model fit first-order absorption (Ka) and elimination (CL) from the central compartment (Vc); a peripheral compartment (Vp and Q); one gut transit compartment (Ktr) to capture absorption delay; and a PBMC compartment to capture TFVdp formation and degradation (K35 and K53, respectively). Parameter estimates (%RSE) were; CL = 51.1 L/h (3.2%); Vc = 223 L (fixed), Vp = 687 L (4.7%), Q = 173 L/h (4.4%), Ka = 1 h<sup>−1</sup> (fixed), K35 = 0.0255 h<sup>−1</sup> (11.1%) and K53 = 0.0269 h<sup>−1</sup> (11.5%). A 600 mg dose was associated with longer absorption delay (Ktr₁ = 1.36 h<sup>−1</sup>) compared to the lower doses (Ktr₂ = 6.1 h<sup>−1</sup>). Vc, Ka and Ktr depend on sampling near C<sub>max</sub> and were fixed to estimations from a separate model using a subset of data with rich PK sampling schemes.</p><p><b>Conclusions:</b> We applied this MIS in curating PK CvT data collected from previously siloed studies into a user-friendly database to support data sharing, management and mining for the community of translational scientists working to optimize HIV therapeutics. Our observation of TFV's dose-dependent absorption delay is a novel finding from the pooled CvT analysis, demonstrating the power to derive new PK knowledge from resources like the HIV PDR.</p>","PeriodicalId":9251,"journal":{"name":"British journal of clinical pharmacology","volume":"90 S1","pages":"16"},"PeriodicalIF":3.1000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bcp.16300","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"British journal of clinical pharmacology","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/bcp.16300","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
21
The HIV Pharmacology Data Repository (PDR): Setting a new standard for clinical and preclinical pharmacokinetic data sharing
Mackenzie Cottrell1, Lauren Tompkins1, Adrian Khoei1, Alexander Tropsha1, Oleg Kapeljushnik2, Robert Hubal2, Julie Dumond1 and Angela Kashuba1
1UNC Eshelman School of Pharmacy;2Renaissance Computing Institute at UNC
Background: Rapidly expanding clinical pharmacology modelling tools can be used to derive biological meaning through in silico study of archived pharmacokinetic (PK) data pools. Yet, a rate-limiting step to employing these approaches is the ability to access high-quality concentration vs. time (CvT) data, aggregated across disparate study designs in a way that is meaningful and usable for PK modelling. This is partly due to a lack of standardization for PK data description. To this end, we defined and applied a minimum information standard (MIS) for PK data description in the development of a web-based database—the HIV Pharmacology Data Repository (HIV PDR)—and demonstrate scientific utility through population PK modelling.
Materials/methods: We defined the MIS with key reportable variables divided into three categories: intervention (drug, route, time and quantify), system (species dosed and anatomical compartment sampled) and concentration (chemical entity and concentration units quantified, including pro-drugs, drugs and metabolites). We identified 610 archived CvT Excel datasets fulfilling this MIS and created data dictionaries to harmonize terminology. The resulting database is stored in an SQL server with the front-end developed using an ASP.NET core with Angular and the back-end on an SQL Server 2017. We extracted CvT values for tenofovir (TFV) and its active metabolite (tenofovir diphosphate; TFVdp) within human plasma and peripheral blood mononuclear cells (PBMC) from study participants dosed with tenofovir disoproxil fumarate (TDF) and fit a population PK model using NONMEMv7.4.
Results: Our data dictionaries collapsed 924 bioanalytical synonyms (analyte name and units) into 145 unique variables with units parsed in a separate column. Additionally, 246 descriptors of species and anatomical compartment were collapsed into 15 and 80 unique variables, respectively, with taxonomical and anatomical hierarchies. The final database aggregates 80 043 CvT datapoints of 77 chemically distinct compounds. Our extracted TDF dataset contained 913 plasma and 708 PBMC observations from 88 human study participants across three dosing levels (150, 300 and 600 mg) under first-dose and steady-state conditions. The final model fit first-order absorption (Ka) and elimination (CL) from the central compartment (Vc); a peripheral compartment (Vp and Q); one gut transit compartment (Ktr) to capture absorption delay; and a PBMC compartment to capture TFVdp formation and degradation (K35 and K53, respectively). Parameter estimates (%RSE) were; CL = 51.1 L/h (3.2%); Vc = 223 L (fixed), Vp = 687 L (4.7%), Q = 173 L/h (4.4%), Ka = 1 h−1 (fixed), K35 = 0.0255 h−1 (11.1%) and K53 = 0.0269 h−1 (11.5%). A 600 mg dose was associated with longer absorption delay (Ktr₁ = 1.36 h−1) compared to the lower doses (Ktr₂ = 6.1 h−1). Vc, Ka and Ktr depend on sampling near Cmax and were fixed to estimations from a separate model using a subset of data with rich PK sampling schemes.
Conclusions: We applied this MIS in curating PK CvT data collected from previously siloed studies into a user-friendly database to support data sharing, management and mining for the community of translational scientists working to optimize HIV therapeutics. Our observation of TFV's dose-dependent absorption delay is a novel finding from the pooled CvT analysis, demonstrating the power to derive new PK knowledge from resources like the HIV PDR.
期刊介绍:
Published on behalf of the British Pharmacological Society, the British Journal of Clinical Pharmacology features papers and reports on all aspects of drug action in humans: review articles, mini review articles, original papers, commentaries, editorials and letters. The Journal enjoys a wide readership, bridging the gap between the medical profession, clinical research and the pharmaceutical industry. It also publishes research on new methods, new drugs and new approaches to treatment. The Journal is recognised as one of the leading publications in its field. It is online only, publishes open access research through its OnlineOpen programme and is published monthly.