From in vitro to in vivo: Effective translation for anti-inflammatory treatments

Researcher in a medical lab

John Unitt, Vice President, Immunology and Inflammation Drug Discovery, at Sygnature Discovery, explores the importance of an effective translational process for preclinical and clinical success.

The global anti-inflammatory therapeutics market was estimated to be $98.26 billion in 2022 and is forecast to reach $146.14 billion by 2032, growing at a compound annual growth rate of 4.1%1. Inflammation underpins or is involved in the majority of human diseases, which is reflected in the rising precedence of treatments targeting inflammation on the market2. With factors such as obesity, type 2 diabetes (T2D), unhealthy dietary habits and population ageing amplifying chronic inflammatory responses, accelerating the rate at which anti-inflammatory drug products progress through to clinical testing stages is essential.

Drug discovery is a complex, integrated, iterative process that transforms a ‘hit’ compound into a drug candidate. This drug compound is then ready to enter preclinical studies, where in vivo disease models are used to test whether a drug is safe and effective as predicted. An optimised, effective and efficient in vitro to in vivo (IVIV) translational process is key to successfully transition from early drug discovery stages to preclinical testing.

Starting with in vitro models

In vitro testing using isolated cells or proteins provides researchers with an efficient starting point for identifying promising drug candidates quickly, helping to validate disease targets and iteratively screening compounds for potential efficacy and cytotoxicity. These cost-efficient, highly controlled and quick approaches can seamlessly integrate into drug discovery projects to deliver critical information to optimise drug performance.

However, mammalian organisms are much more complex than in vitro systems. Despite in vitro approaches providing essential insights into a drug candidate’s mechanism of action, the intricate interplay between various physiological factors and different cell systems still makes accurate predictions of drug efficacy in humans complex. Therefore, once a promising drug candidate has been identified using in vitro systems, in vivo disease models should be introduced. An effective translational bridge is critical to filling the gap between in vitro and in vivo assay understanding. It helps to represent drug efficacy and safety better, enabling a drug candidate’s optimal positioning for clinical success.

A strategy for in vitro to in vivo translation

Developing a robust and consistent translational route to transition from in vitro to in vivo is complicated, with several associated challenges, including navigating the complexity of biological systems and accurately representing the disease target biology in a reproducible model. To overcome these challenges, developing a successful translation strategy is essential, including:

  • Gathering information from in vitro models: Using the knowledge gained from in vitro models ensures that translational in vivo models are valid for the desired disease target and represent the disease pathology and response when exposed to the drug candidates.
  • Harnessing predictive pharmacokinetic (PK) and pharmacodynamic (PD) models: Using both in vitro and in vivo data, in silico models can provide insight into predicting drug PK/PD properties in in vivomodels and human subjects. For example, with predictive information on absorption, distribution, metabolism, and excretion (ADME), optimisation of drug PK, PD, and efficacy in translational in vivomodels can be more efficient.
  • Innovation of in vitro models: The continuous development of in vitro models means increasingly complex systems can provide a more comprehensive and accurate understanding of a drug candidate earlier in the drug discovery pipeline. An example of this is the use of 3D in vitro To advance tumour immunology research, utilising 3D models that mimic the interaction between tumour and immune cells can better predict in vivo efficacy and help screen novel agents3. These in vitromodels better represent the dynamic interplay of different biological systems in vivo.

Having an effective drug translational system to facilitate the transition to in vivo positions a project for success at the later clinical trial stage. By maximising resources, poor candidates are removed in earlier testing stages, which mitigates the risk of failure due to efficacy and safety later in drug development. Production processes are streamlined as only candidates exhibiting promising translational behaviour progress to in vivo testing stages, which saves time and costs.

The value of the LPS model for profiling novel anti-inflammatory drugs

The lipopolysaccharide (LPS) in vivo model has shown promise as a robust and reliable system when developing a drug to block in vivo pro-inflammatory responses. LPS is an immunogenic substance naturally found in the outer membrane of Gram-negative bacteria that in vivo triggers the innate immune response to rapidly generate a range of pro-inflammatory cytokines inter alia.

Optimised to measure pro-inflammatory cytokines (PD) and drugs (PK) present in the blood and other tissues, the LPS model can be used early in drug discovery to evaluate the efficacy of anti-inflammatory drugs at accelerated rates. Helping researchers to better understand inflammatory responses, this model supports drug discovery to target neuroinflammation as well as kidney and systemic inflammation4.

The LPS model further enhances drug target validation by providing in vivo proof of mechanism (POM) and translation from in vitro to in vivo, providing a more accurate overview of how different biological systems relate to each other and respond to drugs.

References

  1. Anti-Inflammatory Therapeutics Market Size, Report 2023-2032. (Cited 6/12/2023). Available from: https://www.precedenceresearch.com/anti-inflammatory-therapeutics-market#:~:text=The%20global%20anti%2Dinflammatory%20therapeutics,4.1%25%20from%202023%20to%202032
  2. Centenary Institute. (Cited 6/12/2023). Available from: https://www.centenary.org.au/health-hub/inflammation/
  3. Mu P, Zhou S, Lv T, et al. Newly developed 3D in vitro models to study tumor–immune interaction. J Exp Clin Cancer Res 2023;42:81.
  4. Skrzypczak-Wiercioch A, Sałat K. Lipopolysaccharide-induced model of neuroinflammation: Mechanisms of action, research application and future directions for its use. Molecules 2022;27(17):5481.

About the author

John UnittJohn Unitt is an experienced pharmaceutical bioscientist. He worked for AstraZeneca R&D Charnwood for over 20 years and was instrumental in the development and success of hit to lead at Charnwood. He has a BSc (Hons) in biochemistry from University of Bristol, a PhD on cardiac bioenergetics from University of Leeds and undertook post-doctoral research on biological NMR at University of Oxford.

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