TWIN4PHARMA

DESCRIPTION

Pharmaceutical companies are facing challenges to deliver their products fast (~ 12 years) and more sustainable, which is often due to excessive costs in experimentation and testing, huge material needs and an untrained workforce.

In particular, the current development and manufacturing paradigm that is based on (1) massive use of trial-and-error methods, (2) tedious and repetitive laboratory testing, (3) outdated scale-up methods and the use of laborious, costly and environmentally harmful (batch) processes

Moreover, only 1 (!) out of 10,000 candidate drugs makes is to the market, after 12 years, with prohibitively large development costs in the order of € 2-3 billions. Moreover, the non-green process technology forces companies to produce mainly in China and India. This leads to supply chain disruptions.

Emerging digital technologies, such as artificial intelligence (AI) and mechanistic modeling help to speed up sustainable process development, and to reduce failures and the experimental burden, thus making supply chains more resilient for a (A) green and (B) digital twin transition and bringing medicines to patients much faster at lower costs. Moreover, production in Europe becomes feasible.

DIGITALIZATION AND SUSTAINABILITY

Advanced digital tools, including predictive modeling and machine learning and hybrid modeling, are being utilized to enhance the efficiency of product and process development. This digital transformation helps to minimize waste and energy consumption in alignment with the sustainable development goals. Moreover, integrating digitalization with sustainability efforts ensures a more environmentally friendly approach to pharmaceutical production, ultimately benefiting both the industry and society.

COMPUTATIONALLY-DRIVEN PHARMACEUTICAL PROCESSES FOR SMALL MOLECULES

Computationally-driven pharmaceutical processes for small molecules, such as flow chemistry, purification, and crystallization, are revolutionizing drug development by enhancing precision and efficiency. Utilizing advanced simulations and predictive algorithms, these processes enable the optimization of reaction conditions and the prediction of crystal structures, reducing the need for extensive empirical testing. This approach not only accelerates the development timeline but also improves the scalability and consistency of pharmaceutical manufacturing.

COMPUTATIONALLY-DRIVEN PROCESSES FOR BIOLOGICS

Computationally-driven processes for biologics, such as freeze-thaw cycles, are significantly enhancing the stability and efficacy of biologic therapies. Advanced modeling and simulation techniques allow for precise control and optimization of these processes, reducing the risk of protein denaturation and aggregation. By leveraging computational tools, the pharmaceutical industry can ensure higher quality and consistency in biologics production, ultimately improving patient outcomes.