Green Chemistry and Biocatalysis
Our R&D is focused on creating synthetic pathways that are not only high-yielding but also environmentally responsible, adhering to the principles of Green Chemistry.
- Sustainable Synthesis: We prioritize atom economy, utilizing high-efficiency catalytic systems and minimizing solvent waste. Our goal is to drastically reduce the Process Mass Intensity (PMI), thereby lowering the environmental footprint of API production.
- Biocatalysis Adoption: We utilize highly selective enzyme-based reactions to replace traditional, harsh chemical steps. This allows for milder operating conditions, fewer process steps, and cleaner intermediate products, resulting in superior purity.
- Solvent Management: Implementing advanced solvent recovery and recycling systems and actively scouting greener solvent alternatives to reduce toxicity and waste generation.
Continuous Manufacturing and Process Intensification
We are moving beyond traditional batch processing to embrace next-generation production methodologies that deliver unprecedented consistency and throughput.
- Continuous Flow Chemistry: Transitioning selected intermediate syntheses to Continuous Manufacturing (CM). This allows for uninterrupted production, reduces equipment footprint, and significantly cuts down manufacturing cycle times.
- Process Intensification: Applying engineering principles to achieve more output with less input (time, energy, materials). This includes optimizing reaction conditions (temperature, pressure) to boost catalytic efficiency and yield.
- Scale-Up Reliability: Using Quality by Design (QbD) principles from the earliest stages to build process understanding, which guarantees reliable and predictable scale-up from pilot to commercial volumes.
Digital Integration and Real-Time Control
Innovation in our quality control is driven by digital transformation, moving from reactive testing to proactive, predictive quality assurance.
- Process Analytical Technology (PAT): Utilizing sophisticated in-line and on-line analytical tools (like Raman Spectroscopy or Near-Infrared, NIR) to monitor critical process parameters (CPPs) in real-time. This provides instant data feedback, allowing for immediate process adjustments.
- AI-Powered Quality: Employing Machine Learning (ML) algorithms to analyze the massive datasets generated by our PAT systems. This helps predict potential process deviations before they occur, ensuring Real-Time Release Testing (RTRT) capability.
- Automation: Implementing robotics and automation in material handling and hazardous reaction steps to enhance precision, operator safety, and throughput while minimizing the chance of human error.