IMI – Oral biopharmaceutics tools project – Evaluation of bottom-up PBPK prediction success part 4: Prediction accuracy and software comparisons with improved data and modelling strategies

Ahmad, Amais; Pépin, Xavier; Aarons, Leon; Wang, Yuya; Darwich, Adam S.; Wood, J. Matthew; Tannergren, Christer; Karlsson, Εva; Patterson, Claire; Thörn, Helena; Ruston, Linette; Mattinson, Alex; Carlert, Sara; Berg, Staffan; Murphy, Donal B.; Engman, Helena; Laru, Johanna; Barker, Richard A.; Flanagan, Talia; Abrahamsson, Bertil; Budhdeo, Shanoo; Franek, Frans; Moir, Andrea; Hanisch, Gunilla; Pathak, Shriram M.; Turner, David B.; Jamei, Masoud; Brown, Jonathan; Good, David; Vaidhyanathan, Shruthi; Jackson, Claire; Nicolas, Olivier; Beilles, Stéphane; Nguefack, Jean-Flaubert; Louit, Guillaume; Henrion, Louis; Ollier, Céline; Boulu, Laurent G.; Xu, Christine; Heimbach, Tycho; Ren, Xiojun; Lin, Wen Hsing; Nguyen-Trung, Anh-Thu; Zhang, Jin; He, Handan; Wu, Fan; Bolger, Michael B.; Mullin, James M.; Osdol, Bill van; Szeto, Ke X.; Korjamo, Timo; Pappinen, Sari; Tuunainen, Johanna; Zhu, Wei; Xia, Binfeng; Daublain, Pierre; Wong, Suet Mei; Varma, Manthena V.; Modi, Sweta; Schäfer, Kerstin; Schmid, Kartrin; Lloyd, Richard C.; Patel, Aarti; Tistaert, Christophe; Bevernage, Jan; Nguyễn, Mai Ánh; Lindley, David; Carr, Robert; Rostami‐Hodjegan, Amin

doi:10.1016/j.ejpb.2020.08.006

Cited by 30 publications

(30 citation statements)

References 19 publications

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“…Many attempts have been made to compare the predictive performances of these GUI‐based software tools. This includes head to head comparison (Parrott & Lavé, 2002), prediction accuracy of multiple software tools in drug absorption modeling (Ahmad et al., 2020), and comparison of different prediction methods (Poulin et al., 2011). There are many factors in the decisions that define the selection of a tool and make one software more dominant in a specific area than others (Figure 7).…”

Section: Discussionmentioning

confidence: 99%

“…However, these users still need considerable prior knowledge and training on the assumptions and equations used for model development to accurately select from a variety of options and to implement and evaluate these models (Jones & Rowland‐Yeo, 2013; Sager et al., 2015; Willmann et al., 2003). Therefore, users have to receive sufficient education on the data and the parameters that are being implemented, and the overall use of the software (Ahmad et al., 2020; Jones & Rowland‐Yeo, 2013). This is further complicated by the fact that definitions and particular routines are sometimes specific to certain modules of these software platforms (Darwich et al., 2017; Lin et al., 2017) and they cannot be transferred between the platforms.…”

Section: Introductionmentioning

confidence: 99%

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Physiological‐based pharmacokinetic modeling trends in pharmaceutical drug development over the last 20‐years; in‐depth analysis of applications, organizations, and platforms

El‐Khateeb

Burkhill²,

Murby

et al. 2021

Biopharm & Drug Disp

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We assess the advancement of physiologically based pharmacokinetic (PBPK) modeling and simulation (M&S) over the last 20 years (start of 2000 to end of 2019) focusing on the trends in each decade with the relative contributions from different organizations, areas of applications, and software tools used. Unlike many of the previous publications which focused on regulatory applications, our analysis is based on PBPK publications in peer‐reviewed journals based on a large sample (>700 original articles). We estimated a rate of growth for PBPK (>40 fold/20 years) that was much steeper than the general pharmacokinetic modeling (<3 fold/20 years) or overall scientific publications (∼3 fold/20 years). The analyses demonstrated that contrary to commonly held belief, commercial specialized PBPK platforms with graphical‐user interface were a much more popular choice than open‐source alternatives even within academic organizations. These platforms constituted 81% of the whole set of the sample we assessed. The major PBPK applications (top 3) were associated with the study design, predicting formulation effects, and metabolic drug–drug interactions, while studying the fate of drugs in special populations, predicting kinetics in early drug development, and investigating transporter drug interactions have increased proportionally over the last decade. The proportions of application areas based on published research were distinctively different from those shown previously for the regulatory submissions and impact on labels. This may demonstrate the lag time between the research applications versus verified usage within the regulatory framework. The report showed the trend of overall PBPK publications in pharmacology drug development from the past 2 decades stratified by the organizations involved, software used, and area of applications. The analysis showed a more rapid increase in PBPK than that of the pharmacokinetic space itself with an equal contribution from academia and industry. By establishing and recording the journey of PBPK modeling in the past and looking at its current status, the analysis can be used for devising plans based on the anticipated trajectory of future regulatory applications.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Physiological‐based pharmacokinetic modeling trends in pharmaceutical drug development over the last 20‐years; in‐depth analysis of applications, organizations, and platforms

El‐Khateeb

Burkhill²,

Murby

et al. 2021

Biopharm & Drug Disp

Self Cite

View full text Add to dashboard Cite

show abstract

“…Physiologically-based pharmacokinetic (PBPK) models are not as easy to use as a smartphone app. As mentioned in the introduction, commercial PBPK software may not be so perfect as a user might believe [2][3][4][5][6]11,35,36]. Before using a PBPK model, we must understand the scientific literacy for mathematical modelling.…”

Section: Part 2: Scientific Literacy For Physiologically-based Pharmacokinetic Modellingmentioning

confidence: 99%

“…In addition, physical chemistry is also important for hepatic clearance, renal clearance, and tissue distribution (including the brain) [85,[168][169][170][171][172][173][174][175][176]. A recent survey suggested that a poor understanding of physical chemistry is one of the reasons for the prediction failure of OA PBPK modelling [6]. A good understanding of the chemical equilibrium [177], nucleation theory [115,178], and fluid dynamics (including mass transport) [172,173,179] is required in OA PBPK modelling.…”

Section: The Critical Role Of Physical Chemistry In Oa-pbpk Modellingmentioning

confidence: 99%

“……Really? Recently, multiple systematic evaluation studies independently suggested that the "bottom-up" predictive power of current oral absorption (OA) PBPK models needs significant improvement [2][3][4][5][6]. Almost all case studies had to use parameter optimization on a drug-by-drug basis to fit the simulated plasma concentration (C p ) -time curve to clinical data (Part 3, section 3.4).…”

Section: Introductionmentioning

confidence: 99%

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Lost in modelling and simulation?

Sugano

2021

ADMET DMPK

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Over the past few decades, physiologically-based pharmacokinetic modelling (PBPK) has been anticipated to be a powerful tool to improve the productivity of drug discovery and development. However, recently, multiple systematic evaluation studies independently suggested that the predictive power of current oral absorption (OA) PBPK models needs significant improvement. There is some disagreement between the industry and regulators about the credibility of OA PBPK modelling. Recently, the editorial board of AMDET&DMPK has announced the policy for the articles related to PBPK modelling (Modelling and simulation ethics). In this feature article, the background of this policy is explained: (1) Requirements for scientific writing of PBPK modelling, (2) Scientific literacy for PBPK modelling, and (3) Middle-out approaches. PBPK models are a useful tool if used correctly. This article will hopefully help advance the science of OA PBPK models.

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Determinant Factors for Passive Absorption of Drugs

Zhu,

Milewski

2023

Oral Bioavailability and Drug Delivery

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IMI – Oral biopharmaceutics tools project – Evaluation of bottom-up PBPK prediction success part 4: Prediction accuracy and software comparisons with improved data and modelling strategies

Cited by 30 publications

References 19 publications

Physiological‐based pharmacokinetic modeling trends in pharmaceutical drug development over the last 20‐years; in‐depth analysis of applications, organizations, and platforms

Physiological‐based pharmacokinetic modeling trends in pharmaceutical drug development over the last 20‐years; in‐depth analysis of applications, organizations, and platforms

Lost in modelling and simulation?

Determinant Factors for Passive Absorption of Drugs

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