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Exploring alternatives to animal testing in drug discovery

Drug Target Review

Animal testing plays a significant role in pre-clinical research and therefore requires the use of millions of animals. million scientific procedures involving live animals were carried out in 2020. million scientific procedures involving live animals were carried out in 2020. In Britain, 2.88 In Britain, 2.88

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4 ways ex vivo models offer Pharma better alternatives to animal testing

Reprocell

Pharma companies are legally required to test novel drugs in animal models before beginning human trials. And while animal testing has progressed thousands of therapeutics that we use today, it is not without its experimental and ethical downfalls.

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Separating the Hype from the Hyperbole Surrounding FDORA’s Alternatives to Animal Testing under the FD&C Act

FDA Law Blog: Drug Discovery

Since 1962, the FD&C Act has authorized FDA to require that sponsors of clinical trials submit data from “preclinical tests (including tests on animals)” in order to demonstrate that their drug is safe enough to advance to testing in humans. adequate to justify the proposed clinical testing.”

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New Alternative Methodologies and Their Applications in Drug Discovery and Development

biobide

Where possible, NAM s seek to avoid the use of animal testing or at least to reduce it. However, reports drawn up as recently as June 2023 still emphasize the necessity of animal testing at some point as we are still far from understanding and reproducing all the variables present in a whole organism in an in vitro or in silico model.

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In Silico for Drug Discovery

biobide

It is fast becoming a cost-effective and efficient alternative to animal testing. It can be used in all stages of drug discovery, from screening to pre-clinical trials and greatly reduces drug development time. The in silico method represents an increasing role in drug discovery as technology becomes more sophisticated.

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4 Unique Challenges of Oncology Trials

Advarra

Innovation Organizations conducting oncology clinical trials face challenges distinct from the rest of the research community. Because of this, oncology researchers may only have access to minimal existing scientific literature related to cell-based data and animal testing to predict an investigational therapy’s effect on human factors.

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Increasing the probability of technical success in drug development using AI and patient heterogeneity

Predictive Oncology

A recent white paper released by Predictive Oncology’s highlights the challenge of late-stage clinical trial failures and the company’s ability to better navigate those obstacles and increase the probability of technical success which is a key metric in target selection, clinical trial design and pipeline replenishment.