Optimization Through Number of Steps and Available Raw Materials

Background:

Our approach to optimizing chemical synthesis pathways, powered by the Sravathi AI platform, addresses the limitations of traditional methods. Conventional route of synthesis (RoS) development is a time-consuming and linear process. It involves a literature survey, prioritization of options, and extensive laboratory work to select and optimize a single or a few routes. In contrast, our AI platform rapidly identifies and evaluates multiple alternative synthetic routes simultaneously. This not only accelerates the discovery process but also focuses on key industrial optimization metrics, such as reducing the number of steps, using readily available starting materials, and employing non-hazardous or non-infringing technology.

Case Study: Nilotinib Synthesis

This case study illustrates how the Sravathi AI platform was used to optimize the synthesis of the drug Nilotinib.

The Challenge:

The "Reported RoS" for Nilotinib is a complex, multi-step process with a lengthy synthetic sequence. It is also known to involve several intermediates that can be difficult or expensive to source. This traditional route presents challenges in terms of cost, time, and manufacturing scalability.

Our Solution: RoS from Sravathi AI

Using our AI platform, we developed a new, optimized Route of Synthesis for Nilotinib that addressed the inefficiencies of the traditional method.

Key Features of Our RoS:

• Reduced Number of Steps: The Sravathi AI-designed route significantly reduces the total number of synthetic steps required to produce Nilotinib.
• Readily Available Starting Materials: The new process was specifically designed to begin with more accessible and cost-effective starting materials (SMs). This reduces dependency on specialized or proprietary intermediates, streamlining the supply chain.
• Flow Chemistry Amenability: A significant portion of the new synthesis is "flow feasible." This means that the reactions can be performed continuously in a microreactor or flow system, which can offer several advantages over traditional batch chemistry, including improved safety, higher efficiency, and better scalability.
• Strategic Reaction Selection: The new RoS utilizes well-established and efficient coupling reactions, such as the Acid-Amide and Grignard/Kumada couplings, along with an N-Arylation/Buchwald coupling. The platform identified a pathway that leverages these robust reactions to build the complex molecule.

Outcome:

By applying the Sravathi AI platform, we successfully developed a more efficient and commercially viable synthesis route for Nilotinib. This optimized process offers tangible benefits, including cost reduction, faster time to market, and improved manufacturing scalability by using fewer steps and readily available raw materials. This case study demonstrates the power of AI in transforming pharmaceutical process development from a slow, linear process to a rapid, parallel-discovery paradigm.