ACL Digital Life Sciences Helped a Global Pharma Leader Reduce Animal Trials Using GAN Models

Overview

A global pharmaceutical innovator—renowned for its breakthroughs in diabetes, oncology, immunology, and neuroscience—sought to improve the efficiency and ethics of its drug development process. With a strategic focus on advanced biologics and patient-centric therapies, the company aimed to accelerate toxicity assessments while minimizing reliance on animal testing.

To support this initiative, ACL Digital Life Sciences collaborated with the client to develop a cutting-edge Generative Adversarial Network (GAN) model that simulates clinical pathology measurements in preclinical studies

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Challenges

Animal testing, particularly on rats, has traditionally been an essential component of toxicity assessments in drug development. Recognizing the need for a more ethical and efficient methodology, the company approached ACL Digital Life Sciences to explore AI-driven alternatives. However, this approach presented several critical challenges:

Regulatory Burden

Extensive documentation and lengthy processes were required to obtain approval for animal trials

Ethical Constraints

Growing scrutiny and ethical concerns around the use of live animals in testing created both reputational and compliance risks

Resource Intensity

Animal trials demanded significant time, cost, and infrastructure, slowing down early-stage drug evaluations

Solution

ACL Digital Life Sciences designed and implemented a robust Generative Adversarial Network (GAN) model tailored to simulate clinical pathology measurements, thereby reducing dependency on live animal trials. The GAN model was trained by mapping the input descriptors (chemical structure, dose, and treatment duration) to the expected clinical pathology outputs. The development and deployment of the GAN model were supported by an MLOps framework. The GAN model was trained using data from the open TG-GATES database, a scientific resource that provides clinical pathology data of rats exposed to various compounds.

This dataset includes:
- Hematological parameters.
- Clinical chemistry parameters.
Chemical Descriptors:
- Represented as a 1800+ dimensional vector.
- Encodes the molecular structure of the compounds.
Dose Levels:
- Classified as low, medium, or high dose.
Treatment Duration:
- Ranges from 3 to 32 days (e.g., 3, 7, 14, 28, 32 days).
Outputs of the GAN Model:
- Clinical pathology measurements typically observed in animal testing.
- Includes parameters such as hematological and clinical chemistry values.

Outcomes

Through ACL Digital Life Sciences’ deep expertise in machine learning and pharmaceutical R&D, the GAN model was developed, deployed, and released in phases—successfully addressing the client’s core objectives. Moreover, the AI-driven approach not only enhanced the client’s scientific capabilities but also set a new benchmark in ethical innovation within the life sciences sector

Ethical Advancement: Significant reduction in the number of live animal trials, aligning with global bioethics and regulatory trends
Accelerated R&D: Faster toxicity assessment timelines enabled earlier go/no-go decisions in drug development
Operational Efficiency: Reduced the administrative and logistical burden associated with preclinical animal studies