Can New Approach Methodologies De-Risk Drug Development?

With tools like right-sized laboratory automation and cell mimics, new approach methodologies (NAMs) can mature to a new default solution in pre-clinical drug development

Nine out of ten newly developed drugs never make it out of clinical trials, in large part because preclinical assessments done in animals do not necessarily translate well to patients. These known failure rates cost money and time. Therefore, the hope is for NAMs to streamline research and development, sorting out best chance candidates for later clinical trial success, and so lowering early-stage costs, while also aligning with growing ethical concerns around animal testing requirements. However, even NAMs cannot fully solve the problem of poor translation. If they can in time justify the hopes put into them, wider adoption should follow, with even more investment in rare disease treatments. 

NAMs are designed to be human-focused; they reduce reliance on imperfect animal proxies, provide relevant data faster in the development process and even can support drug repurposing. For example, patient-derived organoids allow testing drug responses in a genetically diverse population beforehand, thus increasing the likelihood of clinical trial success. Similarly, organs-on-a-chip, 3D cultures and AI-driven toxicity screening offer precise ways to assess drug safety to eliminate ineffective compounds early on. AI and in silico modeling can analyze vast datasets to identify new uses for existing drugs, reducing development time from years to months, as demonstrated with baricitinib, originally approved for rheumatoid arthritis and later for COVID-19, which emerged as potential treatment option from an AI-driven search.¹

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How automation and other tools can facilitate NAM adoption

Automation platforms that improve reproducibility and robustness are key factors for accelerating alternative method adoption in drug development, also making them scalable for drug discovery. There are already case studies that showcase replacement of animal models. For example, nanoparticle-based lung cancer therapies were evaluated in a lung-on-a-chip model, demonstrating feasibility of high-throughput non-animal drug screening.² But a study on 3D liver organoids pointed at the limited stability of the in vitro culture not representing all relevant liver cell types. Both present hurdles in using these systems for long-term, repetitive hepatotoxicity screening.³ 

Though limited to reproductive toxicology, the Tox21 program, for example, has prioritized such alternatives for regulatory testing, but standardized data assembly and integration from various sources to enable regulatory decision-making remains a bottleneck.⁴ NAM data also are not yet as reliably generated as those from traditional models. With automation eliminating variability in NAM assays, regulatory agencies should be more amendable to their use. AI-driven in silico toxicology models have reduced drug testing costs by up to 30% yet require high-quality immune cell data for validation.⁵

Patient-specific drug screening could turn into a new routine, as demonstrated in a personalized cancer therapy study, where patient-derived tumor organoids were used to predict drug responses.⁶ 

Synthetic particles or engineered vesicles are another promising area⁷ that could reduce reliance on animal testing. They can be used as standardized reagents mimicking certain cellular properties such as size, membrane composition, or receptor expression. They help validate that assays are functioning correctly and that detection systems are sensitive and specific to relevant cellular responses. The recent announcement on animal testing requirements by the FDA incentivizes companies to consider alternatives like cell mimics for inclusion in future IND submission packages. Such custom controls for healthy and diseased states can be validated for cell-based assays according to already exisiting recommendations like CLSI-H62, possibly providing a consistent benchmark to compare real biological responses under drug exposure.

This is critical in high-throughput screening (HTS) systems used in NAMs. Refined cell mimic controls can bridge different platforms by offering a standardized input/output, making NAM data more comparable across systems. Using consistent, reproducible controls that help distinguish true biological effects from technical artifacts in parallel with live-cell assays could one day offer insights in mechanistic actions of compounds without needing full animal models.

References:
1.    Richardson P, Griifin I, Tucker C, et al. Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. The Lancet, 2020, 395:e30-e31. doi: 10.1016/S0140-6736(20)30304-4
2.    Huh, D., Matthews, B. D., Mammoto, A., Montoya-Zavala, M., Hsin, H. Y., & Ingber, D. E. (2010). Reconstituting organ-level lung functions on a chip. Science, 328: 1662-1668. doi: 10.1126/science.1188302
3.    Kostadinova R, Boess F, Applegate D, et al. A long-term 3D liver co-culture system for improved prediction of clinically relevant drug-induced hepatotoxicity. Toxicol Appl Pharmacol, 2013, 268:1-16. doi: 10.1016/j.taap.2013.01.012
4.    Thomas, RS, Bahadori T, Buckley T J, et al. The next generation blueprint of computational toxicology at the US Environmental Protection Agency. Toxicol Sci, 2019, 169:317-332. doi: 10.1093/toxsci/kfz058
5.    Luechtefeld T, Marsh D, Rowlands C, Hartung T. Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility. Toxicol Sci, 2018, 165:198-212. doi: 10.1093/toxsci/kfy152
6.    Vlachogiannis G, Hedayat S, Vatsiou A, et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science, 2018, 359:920-926. doi: 10.1126/science.aao2774
7.    Davis PM, Ravkov E, de Geus M, et al. Synthetic abnormal mast cell particles successfully mimic neoplastic mast cells by flow cytometry. Cytometry B Clin Cytom, 2024, 106:437-447. doi: 10.1002/cyto.b.22183