Adopt a modeling approach for preclinical studies
Modeling methods can be used to generate data and shorten development times in preclinical studies.
Modeling approaches are gaining popularity in the biopharmaceutical industry as companies look for ways to keep drug development costs down. Preclinical studies are an area where modeling techniques are applied, but what is the feasibility of moving towards purely modeling techniques for preclinical studies? Will the industry choose to combine a modeling approach with traditional animal studies?
Advantages and disadvantages of animal study
Animal studies have been the reference approach in the biopharmaceutical industry to test new biotherapies in the preclinical phase. According to Lorna Ewart, PhD, chief science officer at Emulate, an American provider of next-generation solutions in vitro models, the advantage of using animals in preclinical drug development is that they provide a dynamic environment to assess drug response in multiple organ systems. Scientists can therefore study the relationship between pharmacodynamics (PD) and pharmacokinetics (PK) necessary to understand efficacy and safety. However, many modern biologic drugs are designed against humanized targets, Ewart notes, which may reduce the usefulness of the animal model because the pharmacology will not be assessed.
“Because genetic and physiological differences exist between species, animal models are unlikely to provide an accurate picture of a drug’s pharmacological and pharmacokinetic properties,” says Ewart. Additionally, animal models of the disease are regularly cited as poor surrogates for the human condition and thus generate pharmacological data that may not accurately translate to human outcomes, Ewart says, pointing out that Alzheimer’s models are a good example of human results being inaccurately translated from animal studies.
Sheng Guo, vice president, data science and bioinformatics, at Crown Bioscience, a contract research organization and a JSR Life Sciences company, says that by providing an effective human surrogate, mouse animal models offer distinct advantages in the preclinical drug development. The similarity of the human and animal model in vivo microenvironments allows PK/PD studies to produce results closely representative of those expected in humans. Mouse models also offer comparability between treated and untreated subjects and are often reproducible, which Guo says is important during preclinical drug development.
“However, these studies can be expensive and present many challenges when trying to screen large cohorts, especially for multifactorial screening. Ensuring modeled studies live are completed under often tight deadlines can also be challenging, as some animal models may need to be restarted, which can take up to three months,” says Guo.
Additionally, the differing growth rates of tumors mean that some may take several weeks to grow to the size needed for these preclinical studies.
“These potential disadvantages should be carefully considered and weighed against the advantages when deciding to start with a study to be modeled. liveGuo says.
The modeling approach
Using a modeling approach in preclinical studies can be beneficial where traditional animal studies can be difficult.
Such models will often be based on historical data from in vivo models accumulated over many years, Guo points out. These models therefore have the potential to save money and time, because the in vivo studies have already been conducted, Guo points out.
“The use of large, carefully curated datasets is essential when using a in silico model approach to get the best insight from preclinical studies. Implementing well-established and easily applicable mathematical modeling methods can further shorten lead times when performing these in silico studies,” Guo says.
Meanwhile, Ewart says a modeling approach using organ-on-chip technology enables scientists to overcome the major limitation of the animal model, as the organ-on-chip model can be created using human cells. . Accordingly, relevant human pharmacology can be explored.
“Since organ-on-chip models are also dynamic, the relationship between pharmacology and pharmacokinetics can be explored over time. Additionally, organ-on-a-chip technology has shown that cells remain functional and viable for longer periods of time, allowing researchers to study longitudinal responses to drugs,” Ewart said.
As models like this become more mature, the endpoints that scientists can measure become more relevant to endpoints done in the clinic, improving the translational value of the model, adds Ewart. .
Particular modeling strategies would make more sense for use in the preclinical stage of biologic drug development. For example, models built from human cells would be preferable, according to Ewart.
For biologic drugs in the preclinical stage of development, strategies should combine empirical data analysis, statistical modeling, and computer simulations in the design and analysis of mouse clinical trials to produce more rational and powerful studies than in vivo Where in silico models alone, adds Guo. According to Guo, these modeling strategies should use comprehensive and diverse data collection that could include:
- High quality and adapted data in vivo studies using similar and related biologic drugs
- Data from in vitro studies using validated and well-characterized cell lines
- Translatable 3D organoid data in vitro models
- Efficacy data
- Toxicity data
- Genomic data
- Fluorescence-enabled cell sorting data.
“Ideally, modeling strategies should be based on methods with previous successful applications where there is an established framework and guidelines for the design, analysis, and application of the resulting mouse clinical trial. Applying these strategies With careful modeling, it is possible to better understand the mechanism of action of biologic drugs and predict their efficacy,” Guo says.
Modeling vs animal studies
Modeling is unlikely to completely replace in vivo studies, Guo said. However, modeling is a powerful approach that can be used to complement preclinical mouse models to help them reach their full potential.
“Modeling can be used to improve mouse model selection, experimental design, data analysis, result interpretation, and biomarker discovery. As a result, more efficient clinical studies, often guided by biomarkers, can be performed,” says Guo.
The use of modeling methods made it possible to establish empirical quantitative relationships between the number of mice and the accuracy of measurements for categorical and continuous efficacy parameters, Guo adds.
“The study of drug efficacy is not currently subject to regulatory guidelines, and it is possible that models may be used exclusively,” says Ewart. “However, evaluating the safety of a drug is a regulatory requirement with strict guidelines in place. The regulatory community still advocates that animal data make a risk assessment and is less likely to support drug progression on modeling alone.
Ewart also points out that until more models go through rigorous evaluation processes and accumulate data across a wide range of therapeutic modalities, the scenario that will yield better preclinical results is one that incorporates data sets. from both animal studies and modeling methods.
Guo concludes that modeling, if applied correctly, can be a useful approach to understanding biologic drugs at the preclinical stage; however, the combination of modeling and mouse studies can be much more powerful.
“In addition to improvements in experimental design and data analysis, this approach may offer faster biologic drug development, bringing critical drugs to patients in shorter timeframes,” Guo said.
About the Author
Feliza Mirasol is the scientific editor of BioPharm International.