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Modeling and simulation have been used in the pharmaceutical industry for over twenty years and can be advantageous for drug sponsors seeking to improve their drug development process and decision making.
Physiologically-based pharmacokinetic (PBPK) modeling and simulation integrates prior knowledge and data generated through the research and development process to inform decisions for the next step of compound development, focusing on understanding and prediction of drug absorption, distribution, metabolism and excretion (ADME) properties of a drug on targeted virtual populations.
With PBPK modeling it is possible to investigate drug concentrations at the site of action that may mechanistically drive pharmacodynamic effects. It is also possible to learn about and confirm driven drug development as featured in current regulatory guidelines.
PBPK Modeling for Drug-Drug Interaction (DDI) Studies
Current draft regulatory guidelines from the EMA and the FDA1-3 testify to the rise of the use of PBPK applications in drug development and the increasing number of PBPK-based drug submissions. The majority (71 percent) of these submissions were related to DDI cases, highlighting the importance of this field.
Using PBPK, it is possible to accurately estimate complex DDI profiles of a compound in silico and explore its possible effects on, for example, cytochrome P450 enzyme (CYP) and uridine glucuronyl transferase (UGT) metabolism as well as on transporter liability before conducting a clinical study. Dynamic DDI simulations can also be used to predict the inhibitor effect at the site of metabolism (gut, liver, or any tissue) and estimate expected inter-individual variability (90 percent confidence interval) for area under the drug concentration-time curve (AUC) and the maximal drug concentration (Cmax).
Using modeling and simulation, it is also possible to fine-tune a DDI study, for example investigating the number of subjects, the minimal dose needed for quantifiable data, the dosing schedule or the effect of dose staggering. This upfront activity can also investigate whether the exposure of a range of potential co-administered drugs is affected by the test drug prior to filing regulatory submissions with the FDA, the EMA and other agencies.
Another advantage is that these studies can enrich the data on a drug label and forego the need for unnecessary additional clinical DDI studies for weak and moderate CYP inhibitors or inducers.
PBPK Modeling for Cross-Species Extrapolation
PBPK models are highlighted in the new EMA guideline1 as a method for integrating relevant data before going to the clinic, to mitigate risks for the first-in-human study. With PBPK studies it is possible to extrapolate knowledge from animal models to human studies for drugs expected to have a narrow therapeutic window and easily change the physiology for extrapolations to another species, gaining knowledge with more confidence from preclinical species to human.
It is also possible to maximize information gain for first-in-class drugs and/or drugs with a limited therapeutic window and extrapolate in-vitro metabolism and transport data to in-vivo values and predict exposures in animals and in humans.
PBPK for Special Populations
Being able to extract as much information as possible from limited data in diseased or special populations is essential in clinical development. PBPK models are the method of choice for predicting drug behavior in, for example, pediatric patient populations and performing clinical trial simulations for a pediatric investigation plan (PIP).
With PBPK, results can be inferred from healthy volunteers to vulnerable patient populations such as newborn babies and the very old. Furthermore, PBPK models assist in making population pharmacokinetic and pharmaco-dynamic predictions for different disease states and age groups.
It is also possible to explore physiological changes in diseased patients and the impact these have on the ADME properties of the drug, enabling the extraction of the most information possible from limited clinical data, thereby bridging data gaps from, for example, sparse sampling, yielding physiology information for informed decisions in drug development.
PBPK for Formulation Assessment and Development
Simulating virtual bioequivalence (BE) trials helps in optimizing study design and formulation development and exploring the ADME of a drug compound after administration via atypical application sites such as lung inhalation or intramuscular and intranasal delivery. Extensive animal studies and human pharmacokinetics still seem to be the “gold standard” in investigational new drug research and bio-equivalency studies. However, using PBPK modelling avoids the cost, time and ethical issues associated with animal experimentation. PBPK models can successfully bypass bio-equivalency studies, predict bioavailability, and drug interactions with in vitro-in vivo correlation that can be extrapolated to humans, thus serving as bio-waiver.4 Additionally, the need for bioequivalence studies can be investigated before conducting a clinical study for existing products. For example, in a recent study a PBPK-based in vitro-in silico-in vivo approach was used as an interesting alternative to tackle and reduce drug product variability in biopharmaceutical quality.5 By coupling in vitro biorelevant dissolution testing in USP-4 Apparatus (flow- through cell) with physiologically-based pharmacokinetic modelling using PK-Sim® software, the performance of seven similar products of carvedilol tablets were compared with the brand-name drug Dilatrend®. Pharmacokinetic profiles were simulated for the brand-name drug and two similar drug products selected according to in vitro observations, in a virtual Caucasian population of 1000 subjects (50% male, aged be- tween 18 and 50 years with standard body-weights). Population bio- equivalence ratios were estimated revealing that in vitro differences in drug release would have a major impact in carvedilol maximum plasma concentration, leading to a non-bioequivalence outcome, thus supporting the need to perform streamlined in vivo bioequivalence studies for these extensively used products.
PBPK for Advanced Applications
The behavior of small- and large-molecule combinations in models for antibody-drug conjugates (ADCs) can be investigated with PBPK modeling while multi-scale modeling enables the investigation of the drug concentration at a target site, thereby supporting proof-of-concept (POC) studies and possibly explaining the effect of concentrations at that site.
Clinical Phase 2 and 3 studies to investigate the efficiency of the compound of investigation are a costly obstacle before a drug can reach the market. In recent years, lack of efficacy and low safety margins were the major causes of Phase 2 and 3 attritions. Thus, support for these clinical studies by M&S approaches is important and numerous examples of how PBPK modelling can help here exist[6]. In a recent example for ADCs, the PBPK model was used to study the impact of antibody distribution and toxophore tissue level simulation on overall distribution and efficiency.7
For ADCs, the free antibody from deconjugation of the small molecule will impact the distribution of conjugated antibodies within the tumor. This was investigated with a PBPK model to analyze the distribution of the clinical ADC Kadcyla in HER2 positive mouse xenografts. This model was able to capture the preclinical results and provided quantitative and mechanistic support further studies to elucidate the impact of multiple mechanisms of action for these complex drugs.
Physiologically-based pharmacokinetic modeling and simulation is becoming “a must-have” for “real” modern drug development processes, where “virtual” simulations complement efficient R&D with safer, quicker and cheaper data.
References
- C. for M. P. for H. U. (CHMP) EMA, “Guideline on the qualification and reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation,” 2016. [Online]. Available: http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_ guideline/2016/07/WC500211315.pdf. [Accessed: 06-Nov-2017].
- C. for D. E. and R. (CDER) FDA, “Physiologically Based Pharmacokinetic Analyses —Format and Content draft Guidance UCM531207.pdf.” [Online]. Available: https://www.fda.gov/ downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM531207. pdf. [Accessed: 06-Nov-2017].
- draft G. FDA, “Clinical Drug Interaction Studies -Study Design, Data Analysis, and Clinical Implications Guidance for Industry,” 2017. [Online]. Available: https://www.fda.gov/ downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM292362. pdf. [Accessed: 27-Oct-2017].
- B. De et al., “PBPK Modeling - A Predictive, Eco-Friendly, Bio-Waiver Tool for Drug Research,” Curr. Drug Discov. Technol., vol. 14, no. 3, pp. 142–155, 2017.
- M. Ibarra et al., “Integration of in vitro biorelevant dissolution and in silico PBPK model of carvedilol to predict bioequivalence of oral drug products,” Eur. J. Pharm. Sci. Off. J. Eur. Fed. Pharm. Sci., vol. 118, pp. 176–182, Jun. 2018.
- EFPIA MID3 Workgroup et al., “Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation,” CPT Pharmacomet. Syst. Pharmacol., vol. 5, no. 3, pp. 93–122, Mar. 2016.
- C. Cilliers, H. Guo, J. Liao, N. Christodolu, and G. M. Thurber, “Multiscale Modeling of Antibody Drug Conjugates: Connecting tissue and cellular distribution to whole animal pharmacokinetics and potential implications for efficacy,” AAPS J., vol. 18, no. 5, pp. 1117–1130, Sep. 2016.