PD Optimizer

PD Optimizer is an AI-driven process development application from Quartic, purpose-built for pharmaceutical and biopharmaceutical scientists and engineers who need to accelerate product and process development without the burden of excessive wet-lab experimentation. By leveraging small-data AI — capable of learning from as few as 10 prior runs or batches — PD Optimizer addresses the core bottlenecks that prevent R&D teams from keeping pace with growing pipeline demands, including the high complexity of biologic molecules, the limitations of legacy DoE and statistical tools, and the inefficiency of traditional tech transfer from process development to manufacturing.

Quartic's PD Optimizer drives accelerated process development by optimizing characterization and control strategies for quality, safety, efficacy, and yield. The platform builds surrogate models using setpoints and outcomes, eliminating the need for mechanistic digital twins or dynamic simulations, and delivers fully digital, GxP-ready records and workflows to support regulatory compliance throughout the development lifecycle.

Core Capabilities

• Scale Dependency Analysis: Identify scale dependencies by optimizing DoE-derived process settings for scale-up campaigns, enabling confident transitions from lab to manufacturing scale.
• Peak Production Monitoring: Track IR spectra in real time to maximize production peaks while minimizing impurities, supporting continuous process improvement during development runs.
• Characterization Guidance: Use AI-driven guidance to set parameter bounds and optimize IR spectral outputs, reducing reliance on manual expert judgment and accelerating design space definition.
• Multi-Response Optimization: Optimize multiple outcomes simultaneously, with the ability to set one response as a constraint while optimizing another, providing greater flexibility in experimental design.
• In-Silico Outcome Testing: Test outcomes such as titer before running a physical batch, allowing teams to explore yield maximization possibilities without safety risk or material waste.
• Historical DoE Data Integration: Leverage existing experimental data, supporting learning from small datasets that are typical at the process development stage.

Key Benefits for PD Teams

• Accelerated development cycles with significantly fewer experimental runs
• Reduced raw material usage and lower overall material costs
• Ability to turn process development output directly into a manufacturing control strategy
• Sample-efficient AI that incorporates human knowledge input without requiring large datasets
• Fully digital and GxP-ready records and workflows for regulatory readiness
• Improved consistency and traceability from process development through to commercial scale

Who PD Optimizer Is Built For

• Process Engineers: Explore multiple parameter combinations without wet-lab runs, use AI to validate outcomes before committing to experiments, and digitally document and transfer successful runs with traceable results.
• Data Analysts: Monitor product performance against design, use PD insights for CMC documentation, and measure deviations against design space for objective batch disposition.
• Quality & Compliance Teams: Design control strategies based on PD intelligence, leverage PD insights for CMC documentation, improve consistency from PD to commercial scale, and enable Quality by Design (QbD) in pharmaceutical development.
• Digital Transformation Leaders: Connect digital strategy to real operational KPIs, accelerate time-to-impact from months to weeks, and build a culture of informed, data-driven decision-making.

Proven Customer Outcomes

• 75% faster process development achieved through AI-driven DoE optimization, with 50% fewer experiments and 100% real-time visibility
• 11% increase in vaccine purity through in-silico optimization in vaccine purification, with 93% product recovery, 40% faster process development, and a 90% reduction in wet-lab runs required

PD Optimizer is delivered as a web-based application requiring no on-premises software deployment — users simply log in to begin designing and testing process development runs securely. The platform is designed to integrate with existing data infrastructure and supports open standards to avoid vendor lock-in, making it suitable for life sciences organizations at any stage of their digital transformation journey.