Amarasekera, Isuru N

Email Address
Research Projects
Organizational Units
Research Interests

Search Results

Now showing 1 - 1 of 1
  • Publication
    Batch Production of a Potent Small Molecule Active Pharmaceutical Ingredient
    (2020-04-21) Amarasekera, Isuru N; Cho, Jinwan; Li, Jason Y
    Small molecule Active Pharmaceutical Ingredients (APIs) have become increasingly relevant in cancer treatment due to their efficacy, targeted treatment, and clinical value. Commonly manufactured in a batch pharmaceutical process, APIs must satisfy Critical Quality Attributes (CQAs) including chemical purity and physical properties. This process involves a multitude of steps, components, and equipment that are optimized to produce an API in a timely and cost-efficient manner. Here, we consider the process and facility design of a batch production of Halfaxia, a new potent anti-cancer drug from Johnson & Johnson. The process begins with a reaction of a starting material and a second reagent in tetrahydrofuran (THF). Following reaction completion, THF is exchanged for ethanol in a technique known as solvent swap distillation. Next, the API undergoes dry seed crystallization in ethanol. The crystals are then filtered out using Nutsche filtration and vacuum drying, producing Halfaxia in powder form. The process involves a 4000-Liter jacketed vessel and a Nutsche filter dryer, as well as heat exchangers, pumps, and pressure vessels for storage. The process will produce 184 kg of API in 77 hours with a 99.8% conversion, which satisfies the objective of producing 100 kg of product. The facility is designed to limit operator interaction and exposure to the API and other chemical compounds that are hazardous to human health. This process design has an NPV of $488 million, an ROI of 400%, and an IRR of 332%, which proves to be very profitable. However, due to confidentiality reasons, the costs of research and development, clinical trials, and FDA approval have been ignored. J&J should pursue further laboratory-scale experimentation and re-run the models using confidential data and figures before the company makes a final decision on the implementation of this process.