GLP-1 agonists have become a crucial component in the treatment of metabolic disorders like type 2 diabetes and obesity. A well-known class effect of these drugs is the prolongation of gastric emptying time, which can influence the pharmacokinetics of other orally administered medications. As a result, evaluating potential drug-drug interactions (DDIs) is a necessary part of the development process for new GLP-1 agonists.

The Challenge: Extensive DDI Studies with Minimal Clinical Impact

The prolongation of gastric emptying caused by GLP-1 agonists is a well-known effect that can impact the absorption and disposition of concomitant oral medications. Typically, sponsors are required to conduct several DDI studies—often involving multiple different substrates—to comprehensively assess these interactions (Table 1). The FDA has traditionally asked for DDI studies with many substrates. While essential for regulatory purposes, these studies can be both time-consuming and costly. Moreover, the interactions can be found to have low clinical relevance, despite the significant resources devoted to them to support the regulatory expectations.

Table 1: Summary of DDI evaluations of the impact of prolonged GET on the PK of concomitant medications for FDA approved GLP-1 agonists programs

GET: gastric emptying time; SC: subcutaneous; PO: per os / oral; NME: new molecular entity; QW: once weekly; QD: once daily; BID: twice daily; PBPK: physiologically based pharmacokinetic

The Solution: Physiologically Based Pharmacokinetic (PBPK) Modeling

After spending 12 years at the FDA focused on physiologically based pharmacokinetic (PBPK) modeling, DDI risk assessment, and model-informed drug development (MIDD), I can say that regulatory agencies definitely recognize the challenges associated with the DDI requirements. Luckily, they are open to the use of alternative methods for evaluating DDIs, and one of the most commonly used is PBPK modeling. This approach allows scientists to simulate how a GLP-1 agonist might affect the absorption and pharmacokinetics of other oral medications, considering the impact of prolonged gastric emptying. Recently, regulatory agencies have considered the PBPK approach as one that could be utilized to evaluate the impact of prolonged gastric emptying on the PK of concomitant medications (Table 1).

Key Benefits of PBPK Modeling

Although regulatory agencies are open to the use of PBPK modeling, that alone does not indicate a reason why it should be utilized over traditional methods of DDI research. Why should you invest in this methodology over the conventional study approach?

• Inform Clinical Studies: By applying PBPK modeling, researchers can optimize the clinical trial protocol development, finetuning elements such as dosage selection, design of PK sampling schedules, allowance for concomitant medications, allowance for organ impairment subjects, etc.
• Reduce Clinical Studies: By applying PBPK modeling, developers can minimize the number of required DDI studies while still providing comprehensive assessments.
• Increase Efficiency: PBPK modeling saves valuable resources by predicting interactions in a virtual setting, reducing the need for extensive (and expensive!) clinical trials.
• Predict Outcomes: The models offer detailed, scientifically grounded predictions about the pharmacokinetic behavior of drugs under different physiological conditions.

Key Considerations of PBPK Modeling:

Not all PBPK modeling tools are created equal, and some consulting services teams are better equipped for DDI studies than others. If you are choosing to move forward with in silico DDI research, here is what you should ask before selecting a modeling platform or consultant.

• What are the mechanisms involved in the DDI? The impact of GLP-1 agonists on gastrointestinal (GI) tract motility is a known class effect. This effect could be measured by various methods with most studies using acetaminophen (1).
• How accurate and reliable are past predictions? Any established software or services company should be able to provide you with examples of past work. For example, GastroPlus is a software that features in developing mechanistic modeling tools to simulate the complex interactions between the gastrointestinal (GI) physiology and various oral dosage forms. Our team has extensive experience in modeling the impact of changes in GI physiology, such as gastric pH (2, 3), gastric emptying time (3-7), complex changes caused by food intake (8, 9), as demonstrated by many scientific publications. It has the capabilities you need—and you can review our Resource Center to see how others have used it for DDI research.
• Can your DDI research be designed to address regulatory concerns and optimized for acceptance? Whether you are conducting DDI research in-house or with a consultant, it is critical to anticipate questions and requirements from global regulatory agencies to increase the likelihood of submission acceptance. The use of GastroPlus, for example, has been accepted by all major regulatory bodies across the world (10-15). If you choose to have an outside organization conduct your modeling, you should ask if they have regulatory experts who can help design the project and advise if needed. Preference should always be given to companies that prioritize providing regulatory insight as part of their work—it demonstrates they are supporting your end goal of submission and acceptance, not just fulfilling their contractual obligations.

Speaking from my experience at the FDA, the PBPK modeling approach should be initiated early in the product development stage and communicated with the Agency to get timely feedback to achieve an optimal outcome. If you are considering the use of PBPK modeling to support the DDI requirements for your GLP-1 agonist compound, let’s connect and discuss any questions you may have. I’m happy to help you get on the right path.

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References:

1. Nakatani Y, Maeda M, Matsumura M, Shimizu R, Banba N, Aso Y, et al. Effect of GLP-1 receptor agonist on gastrointestinal tract motility and residue rates as evaluated by capsule endoscopy. Diabetes Metab. 2017;43(5):430-7.
2. Hens B, Bolger MB. Application of a Dynamic Fluid and pH Model to Simulate Intraluminal and Systemic Concentrations of a Weak Base in GastroPlus(). J Pharm Sci. 2019;108(1):305-15.
3. Bermejo M, Hens B, Dickens J, Mudie D, Paixao P, Tsume Y, et al. A Mechanistic Physiologically-Based Biopharmaceutics Modeling (PBBM) Approach to Assess the In Vivo Performance of an Orally Administered Drug Product: From IVIVC to IVIVP. Pharmaceutics. 2020;12(1).
4. Sutton SC, Nause R, Gandelman K. The impact of gastric pH, volume, and emptying on the food effect of ziprasidone oral absorption. AAPS J. 2017;19(4):1084-90.
5. Statelova M, Holm R, Fotaki N, Reppas C, Vertzoni M. Successful Extrapolation of Paracetamol Exposure from Adults to Infants After Oral Administration of a Pediatric Aqueous Suspension Is Highly Dependent on the Study Dosing Conditions. AAPS J. 2020;22(6):126.
6. Li M, Zhang X, Wu D, Anand O, Chen H, Raines K, et al. Understanding In Vivo Dissolution of Immediate Release (IR) Solid Oral Drug Products Containing Weak Acid BCS Class 2 (BCS Class 2a) Drugs. AAPS J. 2021;23(6):113.
7. Fraczkiewicz G PN, Lavé T, Lukacova V, Bolger MB, Crison JR, Woltosz WS. Modeling Effects of Exenatide on the Pharmacokinetics of Acetaminophen, Digoxin, and Warfarin (https://www.simulations-plus.com/wp-content/uploads/GFraczkiewicz-Modeling_Effects_of_Exenatide_PK_Acetaminophen_Digoxin_Warfarin-AAPS-2008-1.pdf). AAPS; Atlanta, GA2008.
8. Belubbi T, Bassani D, Stillhart C, Parrott N. Physiologically Based Biopharmaceutics Modeling of Food Effect for Basmisanil: A Retrospective Case Study of the Utility for Formulation Bridging. Pharmaceutics. 2023;15(1).
9. Parrott N, Stillhart C, Lindenberg M, Wagner B, Kowalski K, Guerini E, et al. Physiologically Based Absorption Modelling to Explore the Impact of Food and Gastric pH Changes on the Pharmacokinetics of Entrectinib. AAPS J. 2020;22(4):78.
10. FDA. NDA 212725 and 212726 Entrectinib MULTI-DISCIPLINE REVIEW 2019 [Available from: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2019/212725Orig1s000,%20212726Orig1s000MultidisciplineR.pdf.
11. FDA. NDA 210496 Encorafenib MULTI-DISCIPLINE REVIEW 2018 [Available from: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2018/210496Orig1s000MultidisciplineR.pdf.
12. FDA. NDA 208684 and 208685 Deflazacort CLINICAL PHARMACOLOGY AND BIOPHARMACEUTICS REVIEW(S) 2017 [Available from: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2017/208684,208685Orig1s000ClinPharmR.pdf.
13. FDA. NDA 209092 Ribociclib MULTI-DISCIPLINE REVIEW 2017 [Available from: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2017/209092Orig1s000MultidisciplineR.pdf.
14. FDA. NDA205353 Panobinostat CLINICAL PHARMACOLOGY AND BIOPHARMACEUTICS REVIEW(S) 2015 [Available from: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2015/205353Orig1s000ClinPharmR.pdf.
15. FDA. NDA 208434 Alectinib CLINICAL PHARMACOLOGY AND BIOPHARMACEUTICS REVIEW(S) 2015 [Available from: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2015/208434Orig1s000ClinPharmR.pdf.

By Susie Zhang