Bridging in vitro dissolution and in vivo exposure for acalabrutinib. Part II. A mechanistic PBPK model for IR formulation comparison, proton pump inhibitor drug interactions, and administration with acidic juices

Pépin, Xavier; Moir, Andrea; Mann, James; Sanderson, Natalie; Barker, Richard A.; Meehan, E. J.; Plumb, A. Philip; Bailey, George R.; Murphy, Dean S.; Krejsa, Cecile M.; Andrew, Marilee A.; Ingallinera, Timothy G.; Slatter, J. Greg

doi:10.1016/j.ejpb.2019.07.011

Cited by 40 publications

(59 citation statements)

References 39 publications

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“…One claimed benefit of GastroPlus™ is enabling mechanistic comprehension of IVIVC under different conditions [48]. For example, a rifampicin-loaded solidified self-nanoemulsifying drug delivery system demonstrated a good IVIVC level A with a predicted systemic absorption of 96.5% using IVIVCPlus™ module of GastroPlus™ [46].…”

Section: Gastroplus™mentioning

confidence: 99%

In Vitro Dissolution and in Silico Modeling Shortcuts in Bioequivalence Testing

Al-Tabakha

Alomar

2020

Pharmaceutics

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Purpose: To review in vitro testing and simulation platforms that are in current use to predict in vivo performances of generic products as well as other situations to provide evidence for biowaiver and support drug formulations development. Methods: Pubmed and Google Scholar databases were used to review published literature over the past 10 years. The terms used were “simulation AND bioequivalence” and “modeling AND bioequivalence” in the title field of databases, followed by screening, and then reviewing. Results: A total of 22 research papers were reviewed. Computer simulation using software such as GastroPlus™, PK-Sim® and SimCyp® find applications in drug modeling. Considering the wide use of optimization for in silico predictions to fit observed data, a careful review of publications is required to validate the reliability of these platforms. For immediate release (IR) drug products belonging to the Biopharmaceutics Classification System (BCS) classes I and III, difference factor (ƒ1) and similarity factor (ƒ2) are calculated from the in vitro dissolution data of drug formulations to support biowaiver; however, this method can be more discriminatory and may not be useful for all dissolution profiles. Conclusions: Computer simulation platforms need to improve their mechanistic physiologically based pharmacokinetic (PBPK) modeling, and if prospectively validated within a small percentage of error from the observed clinical data, they can be valuable tools in bioequivalence (BE) testing and formulation development.

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Section: Gastroplus™mentioning

confidence: 99%

In Vitro Dissolution and in Silico Modeling Shortcuts in Bioequivalence Testing

Al-Tabakha

Alomar

2020

Pharmaceutics

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“…This allows development of a coherent clinically relevant method and specification story to be available at time of first submission. Although it may not be possible to perform a variant study in vivo, Pepin et al have shown that a validated PBBM model can be produced without the use of a variant study by simply looking across the entire span of in vivo data, which, in this example, included dosing with early and late formulations, dosing with and without acid reducing agents, dosing varying batches of the late formulation, and dosing with orange drink/ grapefruit juice (16). By utilizing the wealth of clinical data, it was possible to predict the outcome of 16 different clinical scenarios with a good level of accuracy.…”

Section: Examples Of Pharmacokinetic Data That Can Be Used To Build Mmentioning

confidence: 99%

“…Although theoretically the impact of drug substance particle size on in vivo dissolution can also be integrated mechanistically, such as through the use of a scaling factor for the diffusion layer model (in Simcyp), there are several aspects to check before using drug substance particle size as a direct input in PBBM (19,20). The assumption of spherical shape for the particles, the potential particle aggregation, and differential wettability according to size may lead to disconnects between drug substance particle size and drug product dissolution (16). In addition, some process parameters may influence dissolution of immediate-release products regardless of the formulation components.…”

Section: Mechanistic or Non-mechanistic Integrationmentioning

confidence: 99%

“…Product batch specific dissolution data should be compared with particle size data of the drug substance used for product manufacture, because the manufacturing process and excipients may lead to changes in the drug substance surface available for dissolution. For a simple drug substance suspension it may be appropriate to utilize just the drug substance PSD as an input to the PBBM if it adequately describes the surface area, but some authors have found a disconnect between the measured PSD and the observed dissolution performance and accounted for it prior to inputting a particle size into PBBM (16,17,21). This has been described by Pepin et al with the P-PSD approach by utilizing a PSD that describes the dissolution performance of a specific batch of product, which takes into account formulationrelated dissolution rate phenomena such as wetting, disintegration, capsule rupture, and deagglomeration and integrates process parameters such as compression force or granulation time, thus providing a more accurate input to PBBM (15,17).…”

Section: Immediate-release Productsmentioning

confidence: 99%

“…When the drug is in its neutral form, the bulk pH should be equal to the surface pH as no acid-base reactions are occurring. When correctly incorporated into the PBBM, the surface pH effect will improve the prediction capability of the model (16,49).…”

Section: Reprinted From Ref 1: Journal Of Pharmaceutical Sciences Pementioning

confidence: 99%

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The Case for Physiologically Based Biopharmaceutics Modelling (PBBM): What do Dissolution Scientists Need to Know?

Gray¹,

Mann²,

Barker³

et al. 2020

Dissolution Technol.

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The purpose of this article is to inform the dissolution scientist of a powerful emerging tool that provides in vivo linkage to dissolution methods. This tool is physiologically based biopharmaceutics modelling (PBBM). Dissolution scientists are mostly concerned with analytical sections of drug development, so exposure to modeling and other pharmacokinetics and biopharmaceutic concepts may be uncommon. PBBM is an in-silico model that focuses on the interactions between the in vivo physiology and the formulation and drug characteristics. The principles behind PBBM is that all mechanisms related to drug release, dissolution, and diffusion from the site of administration to the site of action can be described in a mechanistic way or semi-empirical way. The integration of in vitro dissolution data in PBBM is described, including examples of software and modeling applications. The expertise needed to use the software and appropriate training is discussed. The key inputs that are expected from the dissolution scientist include an understanding of the aqueous solubility across the physiological pH range, impact of in vivo relevant bile salts and phospholipids on solubilization, and the impact of surface pH on dissolution rate. An example of an advanced in vitro system, TNO intestinal model (TIM-1), will be discussed and its importance in establishing a biorelevant understanding of dissolution behavior. PBBM can evaluate the clinical relevance of a dissolution method and justify specifications and ultimately provide an approval advantage to the sponsor. A well-supported dissolution method that provides clinically relevant drug product specifications (CRDPS) will be viewed with favor by the regulators. Therefore, the partnering of a dissolution scientist and biopharmaceutics scientist to develop PBBM is clearly an important step forward in drug development.

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