If a new drug candidate behaves unexpectedly when dosed to animals or humans, it’s likely to delay or even terminate the drug’s development. Therefore, if a drug’s in vivo performance can be predicted, any potential problems can be addressed beforehand.

This is where physiologically based pharmacokinetic (PBPK) modeling software can play an important role. PBPK modeling and its subset, physiologically based pharmaceutics modeling (PBBM), combine physiology, population, and drug characteristics to mechanistically describe a drug’s behavior in vivo. The idea is to simulate ranges of these variables that affect a drug’s movement into, through, and out of the bloodstream. If anything looks inadequate, then improvement strategies can be put in place ahead of the first in vivo dosing to maximize the potential for a drug to hit preclinical or clinical endpoints.

‘Inadequate’ in this case means a failure to meet target drug concentrations in the bloodstream, such as the magnitude, onset, and duration of peak concentrations, which can result from undesirable absorption, distribution, metabolism, and/or excretion (ADME) properties. For example, poor absorption and/or a high degree of drug metabolism can result in only a small fraction of the administered dose reaching the bloodstream, resulting in low drug concentrations. This poor bioavailability can undermine the ability to determine dose-related toxicity and efficacy during preclinical studies. It can also create problems further down the line, such as a high tendency for food effects, drug-drug interactions, and exposure variability.^[1],[2]

In this blogpost, I will highlight how PBPK modeling software can be used early in development to select promising drug formulations and help keep timelines on track.

Predicting cause and effect

Drug developers can use PBPK software to help flag the potential for poor bioavailability at the outset, and to diagnose the root causes after the fact. For example, the likelihood of poor oral absorption causing low bioavailability can be forecasted based on a drug’s solubility, permeability, and physicochemical properties. These properties can be determined using in vitro measurements, or predictions derived from quantitative structure–property relationships (QSPR) using machine learning software, such as ADMET Predictor^®.^[i],[ii] These values can then be inputted into PBPK software such as GastroPlus^® to simulate the rate and extent of drug absorption as a function of dose and physiology.^[iii],[iv] If the simulated fraction dose absorbed is low – that is, most of the administered dose is not expected to pass from gastrointestinal (GI) fluids into the intestinal wall – then the risk of poor bioavailability is high.

Furthermore, PBPK software can forecast the potential for poor bioavailability resulting from high metabolism of absorbed drug in the intestinal wall or liver. For instance, data derived from QSPR modeling, in vitro measurements, and preclinical experiments looking at a drug’s metabolism by enzymes present in the intestinal wall or liver, can all be entered into PBPK software to simulate the rate and extent of absorbed drug passing through the gut and liver to the systemic circulation.^{[v],[vi],[vii]} If the simulated fraction of absorbed drug reaching systemic circulation is low – that is, most of the absorbed drug does not escape metabolism in the intestinal wall or liver – this also poses a bioavailability challenge.

Once a bioavailability risk is detected, PBPK software can be used to help determine promising mitigation strategies. For example, if a drug is expected to have poor oral absorption, simulation outputs can help identify the root cause, such as solubility, dissolution rate, or permeability. If the problem is solubility or dissolution rate, mitigation strategies such as adding solubilizing agents or formulating the drug as a salt, cocrystal, amorphous solid dispersion, or nanocrystals may reduce the risk of poor absorption.^[viii],[ix] If the problem is permeability, approaches such as adding permeation enhancers or formulating the drug as solid lipid nanoparticles may improve absorption.^[x] Examples of mitigation strategies for reducing metabolism include drug chemical modification to create a prodrug, or incorporating enzyme inhibitors in the formulation.^[xi]

Forecasting bioavailability enhancement

Once a formulation mitigation strategy is identified, in vitro testing can be conducted to characterize the properties, such as solubility, dissolution rate, permeability, and metabolism of the enhanced formulation. These properties can be inputted into PBPK software to forecast the magnitude of bioavailability enhancement compared to the original drug form or formulation. If sufficient gains in bioavailability are predicted, drug developers can balance this information with considerations such as cost, market image, and speed to market to choose the best path. If none of these strategies are expected to achieve acceptable results, a different drug candidate or route of administration might be considered. Whatever decision is made, this early forecasting can save time by reducing the need for reformulation or repeated preclinical or clinical studies.

When conducting these early in silico simulations, determining the importance or sensitivity of in vitro and QSPR modeling-derived input parameters is essential. This exercise can be done within PBPK software by conducting parameter sensitivity analyses (PSAs) to determine which parameters have a significant impact on simulation outputs. Since small differences in highly sensitive parameters can cause large differences in results, these parameters should be closely scrutinized. For example, more in vitro or in vivo experimentation may be required to gain confidence in the parameter and the simulation outputs.

The importance of early prediction

The earlier potential problems are identified, the sooner they can be resolved, and timeline delays and costs avoided. This is why, in the absence of a validated model, these early PBPK simulations are valuable for understanding drug, formulation, and physiological parameter sensitivity, and ranking formulation strategies. As preclinical and clinical data are gathered in later development, early PBPK predictions can be refined and validated. This ‘learn and confirm’ process provides a deeper understanding of a drug’s behavior, and enables smarter decision-making throughout development.

PBPK software has many more applications in drug discovery and development.^[xii],[xiii] PBPK modeling and PBBM can be used to select initial doses for first-in-human trials and design biopredictive dissolution methods. They can also aid in the evaluation of food effects and pH-dependent drug-drug interactions. Furthermore, PBPK modeling and PBBM can assist in setting clinically relevant dissolution specifications, and to justify pharmacokinetic study waivers.

This abundance of valuable applications drives the use of PBPK/PBBM throughout drug development, from discovery through approval. It is already recognized as an impactful tool for linking in vitro and in vivo performance, but the field is rapidly evolving and improving. Regulatory agencies, the pharmaceutical industry, academia, and software companies are working together to enhance PBPK/PBBM platforms, and increase their use in drug development. For example, continuous efforts are underway to improve predictions of ADME properties, augment understanding of GI physiology and its incorporation in software platforms, and develop best practices for model development and validation.^[xiv] PBPK modeling is already an incredibly useful tool for keeping drug development on track, and its impact is set to increase in coming years.

If you would like to learn more about PBPK modeling from Lonza, visit their Knowledge Center.

Deanna Mudie
Senior Principal Engineer in Research and Development
Lonza

About Deanna
Deanna Mudie is a Senior Principal Engineer in Research and Development at Lonza’s site in Bend, Oregon, USA. She earned her B.S.E. in Chemical Engineering and her Ph.D. in Pharmaceutical Sciences from the University of Michigan. Since joining Lonza in 2016, Deanna has focused on enabling bioavailability-enhancing amorphous solid dispersion (ASD) formulations by developing dosage form platforms and in vitro/in silico strategies for predicting ASD bioperformance. During her doctoral and post-doctoral work Deanna developed mechanistic in vitro and in vivo drug transport models to predict oral dosage form dissolution and intestinal absorption. Deanna began her career in the pharmaceutical field as an engineer at Pfizer and Merck characterizing, developing and manufacturing oral dosage forms from preclinical to commercial scales.

Notes

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[i] Mudie DM, Stewart AM, Rosales JA, Adam MS, Morgen MM, Vodak DT. In Vitro-In Silico Tools for Streamlined Development of Acalabrutinib Amorphous Solid Dispersion Tablets. Pharmaceutics. 2021 Aug 13;13(8):1257. doi: 10.3390/pharmaceutics13081257.

[ii] Naga D, Parrott N, Ecker GF, Olivares-Morales A. Evaluation of the Success of High-Throughput Physiologically Based Pharmacokinetic (HT-PBPK) Modeling Predictions to Inform Early Drug Discovery. Mol Pharm. 2022 Jul 4;19(7):2203-2216. doi: 10.1021/acs.molpharmaceut.2c00040.

[iii] Miller NA, Reddy MB, Heikkinen AT, Lukacova V, Parrott N. Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies. Clin Pharmacokinet. 2019 Jun;58(6):727-746. doi: 10.1007/s40262-019-00741-9.

[iv] Lin L, Wong H. Predicting Oral Drug Absorption: Mini Review on Physiologically-Based Pharmacokinetic Models. Pharmaceutics. 2017 Sep 26;9(4):41. doi: 10.3390/pharmaceutics9040041.

[v] Heikkinen AT, Fowler S, Gray L, Li J, Peng Y, Yadava P, Railkar A, Parrott N. In vitro to in vivo extrapolation and physiologically based modeling of cytochrome P450 mediated metabolism in beagle dog gut wall and liver. Mol Pharm. 2013 Apr 1;10(4):1388-99. doi: 10.1021/mp300692k.

[vi] Reddy MB, Bolger MB, Fraczkiewicz G, Del Frari L, Luo L, Lukacova V, Mitra A, Macwan JS, Mullin JM, Parrott N, Heikkinen AT. PBPK Modeling as a Tool for Predicting and Understanding Intestinal Metabolism of Uridine 5′-Diphospho-glucuronosyltransferase Substrates. Pharmaceutics. 2021 Aug 24;13(9):1325. doi: 10.3390/pharmaceutics13091325.

[vii] Heikkinen AT, Baneyx G, Caruso A, Parrott N. Application of PBPK modeling to predict human intestinal metabolism of CYP3A substrates – an evaluation and case study using GastroPlus. Eur J Pharm Sci. 2012 Sep 29;47(2):375-86. doi: 10.1016/j.ejps.2012.06.013.

[viii] Bhalani DV, Nutan B, Kumar A, Singh Chandel AK. Bioavailability Enhancement Techniques for Poorly Aqueous Soluble Drugs and Therapeutics. Biomedicines. 2022 Aug 23;10(9):2055. doi: 10.3390/biomedicines10092055.

[ix] Stewart A, Yates I, Mudie D, Pivette P, Goodwin A, Sarmiento A, Winter M, Morgen M, Vodak D. Mechanistic Study of Belinostat Oral Absorption From Spray-Dried Dispersions. J Pharm Sci. 2019 Jan;108(1):326-336. doi: 10.1016/j.xphs.2018.09.031.

[x] Azman M, Sabri AH, Anjani QK, Mustaffa MF, Hamid KA. Intestinal Absorption Study: Challenges and Absorption Enhancement Strategies in Improving Oral Drug Delivery. Pharmaceuticals (Basel). 2022 Aug 8;15(8):975. doi: 10.3390/ph15080975.

[xi] Markovic M, Ben-Shabat S, Dahan A. Prodrugs for Improved Drug Delivery: Lessons Learned from Recently Developed and Marketed Products. Pharmaceutics. 2020 Oct 29;12(11):1031. doi: 10.3390/pharmaceutics12111031.

[xii] European Medicines Agency, Guideline on the reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation. 2020. https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-reporting-physiologically-based-pharmacokinetic-pbpk-modelling-simulation_en.pdf

[xiii] Food and Drug Administration. The Use of Physiologically Based Pharmacokinetic Analyses Biopharmaceutics Applications for Oral Drug Product Development, Manufacturing Changes, and Controls. latest version_guidance (fda.gov)

[xiv] Wu D, Li M. Current State and Challenges of Physiologically Based Biopharmaceutics Modeling (PBBM) in Oral Drug Product Development. Pharm Res. 2023 Feb;40(2):321-336. doi: 10.1007/s11095-022-03373-0.