Improve R&D Sustainability

AI-driven predictive analytics optimizes drug development by reducing inefficiencies, improving success rates, and cutting costs. These advancements lead to faster market entry, higher ROI, and a more sustainable R&D pipeline.

The Problem

Drug development faces significant inefficiencies, with 90% of candidates failing due to inefficacy or toxicity at the clinical trial phase. This results in substantial financial losses, delays in patient treatments, and underutilized R&D investments, limiting companies' profitability and sustainability.

The AI Opportunity

AI leverages historical data to predict drug efficacy and toxicity early, helping companies avoid late-stage failures. This ensures efficient allocation of resources, reduces trial durations, and improves the likelihood of successful market entry.

Organizations using AI-driven approaches have seen success in getting through preclinical stages quicker and cheaper than traditional methods, with some reporting savings of up to 30% in time and cost.

Why It Matters

AI reduces the financial burden of failed trials and accelerates timelines, enabling pharmaceutical companies to optimize their R&D budgets. Beyond profits, this creates a sustainable pipeline by minimizing resource use and improving access to life-saving treatments.

Benefits & Impact

Cost Reduction

AI streamlines the drug development process by identifying high-potential candidates early, minimizing resource expenditure on unsuccessful trials.

Revenue Acceleration

Faster and more efficient trials enable companies to bring new drugs to market sooner, maximizing the period of exclusivity and boosting profitability.

Sustainability Gains

By reducing waste in failed trials and optimizing resource use, AI supports environmentally and operationally sustainable practices in drug R&D, aligning with long-term corporate responsibility goals.

AI Methods & Models

Purpose: Match eligible participants to trials using demographic, genetic, and health data.
Outcome: Faster recruitment and higher retention.
Tools/Models: Logistic regression, clustering algorithms (e.g., k-means), decision trees.

Build Your AI Solution with Amii

As one of Canada’s three national AI institutes, Amii brings decades of expertise, advancing AI innovation and delivering industry solutions to your team. Whether you’re just starting to explore the possibilities of AI or are ready to develop advanced AI models, Amii is here to help.

Training

A successful AI solution requires both technical know-how and a strong understanding of your business. Our training aligns technical and non-technical teams, creating a shared language and fostering the collaboration needed for successful AI implementation.

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Strategy

We collaborate with your team to brainstorm, evaluate, and prioritize AI use cases aligned with your business goals, building your internal capacity along the way. Our experts then validate the top idea, positioning your team for a smooth transition into development.

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Development

Our unique approach places a full-time Machine Learning Resident within your team, supervised by Amii experts, to help build a custom AI solution. After the project, you have the option to hire the resident, ensuring continuity to deployment and expanding your internal AI capacity for future AI innovation.

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