PHARMACEUTICALS tested and the results fed back to the algorithms so that more complex chemical spaces , novel reaction pathways and prioritised routes can be understood and selected . This process aims to improve criteria , such as yield , selectivity and scalability .
This is a continuous circle of information generation that will allow future optimisation without experimentation , therefore reducing development time and improving sustainability as less experimentation needs to be carried out at both lab and manufacturing scale . It is not far-fetched to think that in the future a ‘ ChatGPT for chemistry ’ will be made available to support the work of chemists and engineers .
Computational modelling techniques offer valuable insights into molecular interactions , physicochemical properties and biological activities of drug candidates . Molecular modeling tools , such as molecular docking , molecular dynamic simulations and quantitative structure-activity relationship ( QSAR ) modelling , facilitate sensible drug design and lead optimisation .
By integrating experimental data with computational models , researchers can accelerate decisionmaking processes and prioritise promising candidates for further development . Historical data on SARs and compound properties can enhance the accuracy and reliability of computational models , improving their predictive capabilities .
Role of the CDMO
CDMOs play a vital role in bridging the gap between drug discovery and commercialisation . While they possess extensive data and expertise , they are also committed to maintaining confidentiality and safeguarding intellectual property and clients ’ interests by employing robust encryption protocols , access controls and non-disclosure agreements .
By implementing stringent data protection measures , CDMOs can uniquely compare data and leverage historical knowledge without compromising confidentiality . Historical data stored within CDMO databases can inform future projects , allowing researchers to build upon past successes and avoid repeating past mistakes .
CDMOs must use the data collected in conjugation with adoption of the 12 Principles of Green Chemistry , which provide a framework for designing eco-friendly processes and minimising environmental impact . 4 By prioritising principles such as waste prevention , atom economy and renewable feedstocks , pharmaceutical companies can reduce carbon footprint , conserve resources and promote sustainable innovation .
The introduction of these principles is also aligned with the need to avoid the use of restricted substances when developing a new drug . When we cannot avoid using them , this represents additional complexity . The use of these substances must be kept to a minimum and additional controls need to be implemented . Historical data on past synthetic routes and manufacturing processes can inform the development of greener , more sustainable alternatives , leading to reduced environmental impact and improved efficiency . 5
Hovione has developed an internal tool that is helping us to leverage our knowledge to speed up drug development while leveraging the historical data generated in the past 65 years of taking new drugs from lab to commercial scale .
Case study : PRIME
The increasing speed and complexity of developing an API presents a challenge to determine its sustainability . While we can increase the speed of development , how can we be sure that we are also developing a sustainable process ? Do we know if we reached the process limits , i . e ., maximum yield , lowest volume , less waste , etc .? Sometimes , due to time constraints , the answer is ‘ No ’.
Therefore , a team at Hovione decided that a better approach could be to identify the target to be achieved and then leverage the historical data to achieve it . This was done by looking at the progress of a project in the different development phases ( Figure 1a ) and comparing the final endpoint across various projects ( Figure 1b ).
This initial concept guided Hovione to the creation of our internal tool PRIME ( Process Ranking of Inputs from Manufacturing and Environment ). PRIME enables the comparison , evaluation and assessment of different projects using established sustainability and efficiency metrics like E-factor , process mass intensity ( PMI ) and atom economy ( Figure 2 ). These metrics generate a process score and therefore establish pathways to achieve a faster and more sustainable development based on historical data .
This tool also allows Hovione scientists to have an overall view of the process and guide the development , with a focus on the more critical aspects of each step
Figure 2 – Snipping of Hovione PRIME tool
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