In case you missed it: CHI + Certara Webinar on Artificial Intelligence (AI) -Powered Model-Informed Drug Development (MIDD)

MIDD has transformed the pharmaceutical landscape—empowering scientists and researchers to make smarter, faster, and more confident decisions. By leveraging predictive models, MIDD reduces risk, saves time, and helps bring safer and effective treatments to patients.

Now, with the rise of AI, MIDD is entering a new era. AI is not just a supporting technology—it’s a driving force, enhancing model precision, accelerating discovery, and improving outcomes across the entire drug development lifecycle.

But in a highly regulated industry like drug development, innovation alone isn’t enough. It must be applied responsibly, grounded in rigorous science, validated for its intended use, and aligned with evolving regulatory frameworks. This is where Certara stands apart. In this blog, we explore how AI and MIDD work together to modernize pharmaceutical Research and Development (R&D), and how Certara ensures that this innovation is credible, compliant, and impactful.

At its core, MIDD leverages mathematical models and simulations to inform drug development decisions. Models constructed from chemical, biological, pharmacokinetic (PK), pharmacodynamic (PD), safety and efficacy data generate insights that optimize clinical trial design, inform dosing strategies, and help understand benefit-risk profiles. These models may be empirical (“follow the statistical trends”), mechanistic (“follow the underlying biology”), or AI-based (“follow the hidden pattern”).

MIDD enhances traditional approaches by maximizing the value of available data and enabling simulation-based exploration of unstudied scenarios and populations. By placing simulation, computation, and real-world data at the center of decision-making, MIDD supports more informed, efficient development strategies—helping reduce uncertainty, streamline timelines, and manage costs across all phases of drug development.

AI doesn’t replace current modeling approaches—it enhances and accelerates them.

Whether you’re a pharmaceutical scientist, AI researcher, or drug development leader—AI-enhanced drug development will reshape your world by helping you eliminate inefficiencies, reduce resource demands, accelerate innovation, and improve patient outcomes.

From automating time-consuming tasks to extracting insights from massive datasets, AI helps researchers identify patterns that were previously out of reach. Deep learning can enhance model accuracy, while generative AI (like Large Language Models (LLMs) and Generative Pre-trained Transformer (GPT) tools) enables faster and more efficient validation of complex Quantitative Systems Pharmacology (QSP) models. These mechanistic, computational models integrate biological, pharmacological, and disease data to predict drug behavior and effects across different scales—from molecular pathways to clinical outcomes.

AI is also playing a growing role in regulatory interactions—helping generate model-based evidence that supports decision-making and improves confidence among regulators.

Here are six key areas where AI is accelerating MIDD in real-world applications:

1. Drug discovery
AI identifies new targets and promising compounds faster than traditional methods. Deep learning methods can predict molecular behavior and therapeutic viability, boosting hit-to-lead ratios and accelerating entry into in vivo studies. Generative AI can create novel compounds optimized for project goals, freeing researchers to focus on other tasks.

2. Clinical trial optimization
MIDD models developed with early clinical data help predict safety and efficacy for different patient populations which leads to optimization of trial design and patient inclusion criteria. AI/ML techniques applied to RWD can help in site and patient selection resulting in more efficient and cost-effective trials.

3. Personalized medicine
AI integrates genetic, biological, and real-world data to tailor treatments for individual patients—improving dosing accuracy, safety, and efficacy.

4. Safety and efficacy analysis of Real-World Data (RWD) and trial outcomes
AI analyzes large datasets to identify safety trends and evaluate efficacy in diverse populations treated with the same drug—enhancing inclusivity and drug safety. These analyses can often inform further trials designed to expand a label to include new indications or new populations.

5. QSP model validation
Generative AI can streamline the validation of quantitative systems pharmacology models, supporting faster exploration of therapeutic potential.

6. Regulatory strategy and compliance
AI offers significant opportunities to improve the efficiencies and capabilities of MIDD—but only when paired with a deep understanding of regulatory expectations. Certara’s AI-powered solutions are built on a foundation of regulatory science, helping sponsors produce model-based evidence that aligns with agency guidelines and supports faster, more confident approvals.

While AI and MIDD offer extraordinary promise, their success ultimately depends on navigating a highly regulated environment with care and precision. That’s where Certara excels.

Our global team of scientific and regulatory experts ensure that every AI-enhanced model, simulation, or development strategy is aligned with regulatory expectations. From INDs to NDAs, we help sponsors design evidence generation strategies that meet the highest scientific rigor and regulatory compliance standards.

This ability to balance cutting-edge innovation with real-world constraints is not just a differentiator—it’s essential to advancing safe, effective therapies in today’s complex landscape. With over two decades of experience supporting regulatory submissions across the U.S., Europe, and Asia, Certara helps ensure that innovation is both impactful and approvable.

Integrating AI into MIDD comes with challenges, including:

• Algorithmic and data bias
• Data quality concerns
• Technical adoption barriers

However, the path forward is promising. As data standards improve and cross-disciplinary collaboration grows, AI will further enhance the transparency, reproducibility, and regulatory acceptability of model-informed approaches.

At Certara, we are actively partnering with industry leaders, academic collaborators, and regulators to advance the responsible adoption of AI in drug development. We see the future not just as automated—but as strongly guided by science and policy.

True progress only happens when innovation meets insight—and regulation. At Certara, we don’t just adopt new technologies—we apply them within the context of decades of regulatory and scientific experience to deliver real-world impact.

Are you ready to bring MIDD and AI into your workflow? Start by learning from our experts.