Utilization of Physiologically Based Pharmacokinetics Modeling in the Development of Orally Administered Formulations Intended for Systemic Delivery of Small Molecules

By: Anders Lindahl, PhD and Jan Neelissen, PhD

An oral physiologically-based pharmacokinetics (PBPK) in silico model focuses on the link between a molecule’s physiochemical properties, formulation parameters and plasma exposure (including Cmax, Tmax, T1⁄2 and area under the curve (AUC)). Such a PBPK model can also be referred to as a physiologically-based biopharmaceutics model, (PBBM).(1) It is based on mechanistic models that divide the gastrointestinal tract into several compartments, corresponding to the stomach, duodenum, jejunum, ileum, and colon.

There are commercially available PBPK software packages that can create these individual compartments, each having individual characteristics based on their physiologically relevant length, radius, volume, pH, bile acid concentration and transit time depending on species. Disintegration, dissolution and precipitation of the active pharmaceutical ingredient (API) from a formulation and the consecutive absorption from each compartment are described by differential equations. 

The extent of modeling depends on the amount of data available, but it can be a useful tool during early drug development (Figure 1) and improves as more data becomes available.

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Making Informed Decisions in Early-Stage Candidate Selection

At the very early stages of drug development (candidate selection), when little data is available, integrating the dissolution and absorption profiles with API characteristics (pKa, logP, solubility) and permeability (Caco-2 or MDCK) combined with gastrointestinal physiology (gastric emptying, transit time, etc.) allows for the prediction of the fraction absorbed (Fa) after oral administration of a solution or suspensio

This is especially useful at the early stages of oral candidate selection where PBPK modeling can help developers choose the molecule(s) with the best properties most likely to attain sufficient absorption, or choose the molecule(s) whose absorption challenges are addressable by formulation technologies. 

With the use of a parameter sensitivity analysis, the formulator can identify how formulation and API properties, such as particle size, solubility, or permeability, affect absorption (Figure 2). This will support candidate selection and decrease the need for iterative formulation and in vivo pharmacokinetic (PK) studies during the development process of a candidate drug. 

When animal/human metabolic clearance derived from in vitro microsomal and/or hepatocyte studies is incorporated in the PBPK model (Figure 1), the first pass loss (FPL) in the liver can be estimated as well as the animal/human bioavailability. Even if there is very high absorption, a high FPL in the liver will limit the oral bioavailability, and this problem cannot be solved with formulation design. It is therefore important for the formulator to understand the FPL of the candidate molecule(s) early in the product development process.

Confirming Formulation Technology Selection

The first animal species/human PBPK model allows the formulator to select which formulation technology may be used for the first in vivo animal PK studies. For example, the first PK studies may plan to use mouse species, and an initial PBPK model may predict that a solution formulation would result in 100% absorption. Once the mouse PK data dosed with a solution is available, the existing mouse PBPK model can be updated with measured parameters, as shown in Figure 3A. 

Intravenous (IV) data are essential, as they provide estimates of volumes of distribution (Vd) and clearances (Cl), which are the PK parameters that describe the fate of a compound after entering the systemic circulation (Figure 3B), as well as allowing the calculation of absolute bioavailability.

After the addition of the known Vd and Cl into the existing mouse PBPK model, if the oral exposure prediction is off, as represented in Figure 3C, where the Cmax and AUC are slightly overpredicted, a parameter sensitivity analysis can be performed to investigate which parameters may need adjustment for a better fit (Figure 3D). Once a better fit is obtained (Figure 3E), this will allow the PBPK model to provide estimates of Fa, the fraction of drug escaping liver metabolism (Fh), FPL and bioavailability (F).

In this example, the solution formulation resulted in 100% fraction absorbed, while bioavailability is limited by FPL in the liver (59%) resulting in a bioavailability of 41%. Once a good first animal species PBPK model is obtained, it can be validated against PK data from a second species. If the predicted oral (PO) profile fits the measured data in the second animal species, confidence in predictability for human absorption is increased.

In the above example, although a solution formulation performed well, this may not be a suitable formulation for high-dose animal toxicology or human studies, and the human PBPK model can also be used to predict how a solid dosage form may perform. If the Fa is predicted to be reduced in humans, then the effect of particle size reduction can be modeled, and if still not sufficient, a solubility-enabling technology may be needed, such as lipid-based formulations or amorphous dispersions.

uld be tested in the same way as described above, but studies may also include in vitro dissolution profiles with different prototypes, the results of which can then be integrated back into the PBPK model (Figure 1).(2) This is a valuable tool when designing the PK study and choosing the correct formulation. 

At this stage, further advanced estimations of Vd and Cl may be performed and again, inputted into the human PBPK model, and additional methods such as in silico, in vitro and/or allometric scaling can be used for this purpose.(3-6) Combining validated animal PBPK models with estimates of human Vd and Cl results in a more reliable human PBPK model.

Optimizing Formulation Development

Once a suitable formulation technology is selected, new formulations can be made to optimize the current prototype, including in vitro dissolution profiles, which can be inputted into the PBPK model to predict the outcome, followed by in vivo PK studies in an animal species, with the aim to select the optimal one for first-in-human studies. Finally, the human PBPK model can be updated with human PK data, allowing for other assessments such as the design of modified release products, where a release profile can be derived that matches the desired in vivo PK profile.

PBPK models are most valuable when built as early as possible in drug development, however they can also be used in later stages to investigate how to increase drug bioavailability in humans. Figure 4 shows such an example, where a human PBPK model was created to determine the fraction of the drug absorbed. In this instance, the model confirmed that fraction absorbed was much lower than 100% because of the original formulation selected. Using the formulation technology selection process described previously, modeling allowed the development of a new formulation, which, when tested in human clinical trials, validated the model and led to a drug product with a much higher systemic exposure.

Another advantage of PBPK modeling is that a model can be used to establish the correlation between the in vitro dissolution from formulations and the corresponding in vivo dissolution (IVIVC), and the resulting plasma concentration-time profiles in humans. When properly validated, this IVIVC may be used to waive future clinical bioequivalence studies, such as those commonly used during late-stage development, and for possible post-approval changes.(7-8) However, in order for such modeling to be used for regulatory purposes, the platform (i.e., the software) for the PBPK model must be evaluated by scientists, before submission of the model to the regulatory authorities for qualification.(9-11)

Conclusions

Investing in PBPK early in the development process helps minimize development risk by identifying molecule liabilities, and facilitates product development decision-making. By doing this, resources can be focused on drugs with the greatest chance of success in clinical trials. It is vital that the models are updated and refined during the process with in vitro and in vivo data so that the model can be validated and its efficiency increased – this ultimately can lead to development times being reduced, and drug products that are efficient and patient-centric.

References

1. Bermejo et al 2020. A mechanistic Physiologically-Based Biopharmaceutics Modelling (PBBM) Approach to Assess the In Vivo Performance of an Orally Administered Drug Product: From IVIVC to IVIVP . Pharmaceutics 2020 Jan 17;12(1):74 https://doi.org/10.3390/ Pharmaceutics12010074
2. Jamei et al. 2020. Current status and future opportunities for incorporation of dissolution data in PBPK modeling for pharmaceutical development and regulatory applications: OrBiTo consortium commentary. European Journal of Pharmaceutics and Biopharmaceutics. 2020 (155), 55-68. https://doi.org/10.1016/j.ejpb.2020.08.005
3. Lombardo et al. 2004. Prediction of Human Volume of Distribution Values for Neutral and Basic Drugs. 2. Extended Data Set and Leave-Class-Out Statistics. J Med Chem 47:1242- 1250. https://doi.org/10.1021/jm030408h
4. Rodgers and Rowland; 2007. Mechanistic approaches to volume of distribution predictions: understanding the processes. Pharm Res. 24(5):918-933. https://pubmed.ncbi. nlm.nih.gov/17372687/
5. Sohlenius-Sternbeck et al. 2010. Evaluation of the human prediction of clearance from hepatocyte and microsome intrinsic clearance for 52 drug compounds. Xenobiotica, 40:9, 637-649. https://doi.org/10.3109/00498254.2010.500407
6. Mahmood 2007. Application of allometric principles for the prediction of pharmacokinetics in human and veterinary drug development. Advanced Drug Delivery Reviews. 59, 1177- 1192. https://doi.org/10.1016/j.addr.2007.05.015
7. U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER). 1997. Guidance for Industry. Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro/In Vivo Correlations.
8. European Medicines Agency, 2014. Guideline on the pharmacokinetic and clinical evaluation of modified release dosage forms. EMA/CHMP/EWP/280/96 Rev1
9. U.S. Food and Drug Administration 2019. Physiologically Based Pharmacokinetic Analyses — Format and Content Guidance for Industry [Internet]. U.S. Food and Drug Administration. Available from: https://www.fda.gov/regulatory-information/search- fda-guidance-documents/physiologically-based-pharmacokinetic-analyses-format- Andcontent-guidance-industry
10. European Medicines Agency 2019. Reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation [Internet]. Available from: https://www.ema.europa.eu/ en/reportingphysiologically-based-pharmacokinetic-pbpk-modelling-simulation
11. Suarez-Sharp et al. 2020. Translational Modeling Strategies for Orally Administered Drug Products: Academic, Industrial and Regulatory Perspectives. Pharm Res (2020) 37:95 https://doi.org/10.1007/s11095-020-02814-y

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