Drug development is an essential process in the pharmaceutical industry, however, it is also time-consuming and costly which contains preclinical, clinical and after-market. Finding new ways of developing and speeding up the process is important, and one of the solutions that have gained traction in the past few years involves quantitative methods and modeling (QMM), which covers a broad spectrum of tools that can be used in modernizing generic drug development. (1) Modernizing and improving this process is among the priorities for the pharmaceutical industry, especially during times like the COVID-19 pandemic.

QMM is associated with many different tools for modernizing drug development and product lifecycle management. In the drug development system, mathematical models are employed to integrate data and make predictions; these models include a wide variety of data related to the drug product, such as the formulation, in vitro/in vivo release, pharmacokinetics (PK) and pharmacodynamics (PD), the clinical responses, and more. QMM provides a way of aiding decisions in business and health care supplies and making the process of drug development and adoption faster.(2) This can be especially important for drugs that have a wide-reaching health impact, like generics, and treatments for rare conditions that don’t get a lot of funds or attention. 

Modeling and simulation are being increasingly used in drug development because they provide the opportunity to synthesize information and extrapolate beyond existing research. This has led to the creation of model-informed drug development (MIDD), which is an approach used for both generic and brand products. MIDD considers everything that is known about the drug and provides a pathway for quantitative risk modeling.(3) Essentially, it uses data in complex and sophisticated ways to make predictions that are certain and lower the risk of negative outcomes.

MIDD can use large data sets and support organizations like the FDA in their work. This model can be employed for scientific and regulatory purposes across the stages of drug development, starting with new drug applications, through NDAs, ANDAs, and post-approval evaluation. This means that it can be deployed at multiple points of the process. (3)

Quantitative systems are proving to be increasingly significant and their potential impact is quite large. They are being recognized and utilized more, especially as the drug development processes need to be faster. They can be used to generate hypotheses and create understanding of new ideas and new drugs. (4)

QMM uses empirical models, which has significant advantages. First, it can be used for interpolation and making predictions based on existing data. Secondly, it provides a stronger base for predictions involving novel situations, as it uses physics, mathematics, and physiology for this goal. (5) Researchers can have an idea of how a drug might be used or how its effects could be applied for a new condition, however, testing these ideas is long and expensive. Therefore, QMM can provide a way of pre-testing them, avoiding unnecessary human trials and a waste of time. Essentially, these methods provide a way of making predictions by taking advantage of complex data and catch details that human observers would easily miss. 

Furthermore, QMM can be especially useful for generic drug development and is often discussed in regards to the same. As the data from the brand project will usually be available, it becomes a path to cutting costs and times in developing a generic, as it provides an empirically sound approach that can streamline processes and increase the speed without the drug being unsafe. QMM can provide faster access to generic, speed up the decision-making processes, reduce the need for human studies, employ bioequivalence method, leading to overall better decisions in the process of review. (5)

Generic drugs generally appear in small markets, which require more efficient development to stay ahead of the competition and also to make a positive impact, as generic drugs are more available to the general public. Using new methods based on quantitative systems will allow to change the market of generic drug development so that drugs based on complex products are developed faster and more efficiently and can hit the markets faster as well. (5)

In addition, QMM is not only related to drug development. It can be used for other purposes, for instance, for tracking the natural history of rare diseases such as the quantitative retrospective natural history modeling (QUARNAM), which can be used to better understand very rare disorders to develop interventions, provide counseling, and understand the disease. This method can also be used on a case study as a meta-analysis can be used to understand a published study, as it provides a sophisticated method for analyzing cases.

The advantage of QUARNAM is fast and requires a lower effort, which can answer how long it takes to make the diagnosis, how long the patients live, what factors can predict disease severity, and where patients can be recruited for study. By understanding rare conditions, this method can also provide an avenue for developing new solutions and treatments. (6)

On the other hand, the pharmaceutical industry in general makes more and more use of digital technologies, and this is a trend that shows no signs of stopping. In particular, there is a real push to outsource tasks that take too much time and that require complex data analysis and pattern recognition. There is also a significant drive to make drug development faster and more efficient, which means being able to obtain results quickly and use available data better. The COVID-19 pandemic, in particular, has shown the need for faster solutions, but beyond this, the generic drug market and others require better solutions that help get the necessary medication out safely, starting before the development process and ending after its release.

In the future, it seems likely that QMM will continue to develop and become a much more widely used part of the drug development process. As Artificial Intelligence (AI) and other digital tools become more accessible, methods for approaching drug development also become more significant. However, COVID-19 and the resulting push towards digitalization provides an opportunity to see that digital solutions can be effective.

Today, more organizations will be able to employ QMM, which is being promoted at the moment by institutions like the WHO or the FDA, so it is becoming more recognized  in modernizing the generic drug development and review.

References

1    Clinical Pharmacology & Therapeutics (February 2019), Volume 105, Number 2

2    FDA. (2020). https://www.fda.gov/drugs/regulatory-science-action/impact-story-modeling-tools-could-modernize-generic-drug-development

3    FDA. (2017). https://www.fda.gov/drugs/news-events-human-drugs/leveraging-quantitative-methods-and-modeling-modernize-generic-drug-development-and-review-public

4    Pharmacometrics & Systems Pharmacology (2015), 4(2), 91-97.

5    FDA (2017).  https://www.fda.gov/media/108564/download

6    J Inherit Metab Dis. (Aug 26. 2020 ) Online ahead of print.

About the Author

Lin Zhang, M.D., senior director of a health care industry company in the United States. With the experience in clinical medicine, biotechnology, health industry and other fields, he is responsible for the research and development of plant medicine, functional food and health products. He was a clinician and worked for the National Cancer Institute, FDA and the National Cancer Center of Japan for many years.

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