Testing of drug candidates in animals for safety and efficacy was first required in the United States in 1938. A major update to these requirements occurred in late December 2022, when President Biden signed legislation to open the way for the US Food and Drug Administration (FDA) to consider applications for human clinical trials without animal studies having been conducted. Now, it is possible for the FDA to allow drug candidates to progress to human trials based on non-animal (in vitro and in silico) data.

Testing in animal models, in accordance with Good Laboratory Practice (GLP) requirements, is expensive and adds significant time to the development cycle. As such, it is on the critical path to submittal of an Investigational New Drug (IND) application, which must be received prior to initiation of human clinical trials.

If animal studies are not required, the entire early drug-development phase through to IND filing could potentially look very different. Timelines for the development of safety packages and manufacturing of material for testing purposes may, in fact, be transformed.

The potential for candidates to progress to clinical trials using in vitro and/or in silico testing raises several questions.

Firstly, does it afford the opportunity to reach the IND filing stage more quickly and thus ultimately get new products to patients sooner? Due to the time savings associated with limited or no animal studies, it is highly likely that the IND-enabling timeline would be reduced. A robust risk assessment will still be needed, so in vitro studies are likely to be more extensive, but it is reasonable to expect the timeline to IND to be reduced if GLP toxicology studies are not required.

A portfolio of assays will need to be completed in place of animal tox studies. However, such tests generally take less time and can be performed in parallel, which could create further measurable time savings.

The largest requirement for drug substance in preclinical development today is for in vivo animal studies. If no animal testing is done, will there be reduced preclinical material requirements? If so, does that alter the necessary scale of pilot batches, which in turn could measurably reduce the cost of early development? Will pilot batches become more important, because the material required to generate analytical data will be needed sooner?

Of course, the same quantity of GMP material will be required for human clinical trials. If the preclinical phase is accelerated, will sponsors need to establish GMP-compliant manufacturing capabilities earlier than they do today?

A consideration of these various factors leads to the conclusion that while manufacturing is not generally on the critical path to IND and human clinical trials when GLP tox studies are involved, it may be on the critical path for programs that seek FDA approval of their INDs based only on non-animal data. Indeed, it may be necessary to re-map the critical path. Representative material will still be required for reference and stability testing, but the order in which tests are performed could be rearranged.

Such changes could potentially increase flexibility while also having dramatic impacts on early-phase development activities – but this won’t happen overnight. At present, getting a drug candidate to IND filing without animal testing is the exception rather than the rule.

For those sponsors that are interested in considering programs leveraging in vitro and in silico safety and efficacy testing, Lonza is ready to help. We have extensive, well-established in vitro testing capabilities and are well-positioned to respond to the need for flexibility.

Regardless of the route our customers wish to pursue, we have the experience to help them identify solutions for optimizing and streamlining early development programs from both the safety testing and manufacturing perspectives. We leverage our global network and capabilities to provide tailored programs designed to meet the specific needs of our customers’ molecules and priorities with respect to timelines, yield optimization and risk.

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