Quantifying Biopharma Trend Dynamics

A Segmented Analysis of CMOs, CGTs, and Pharma

Jason Beckwith ; Paul Rooney ; Stephen Goldrick ; William Nixon, PhD; Stavros Kourtzidis .

Introduction

The research aims to address critical questions related to the workforce in biomanufacturing, with a focus on talent management and skills deficits in the evolving biopharma landscape. The first question explored trends and dynamics characterizing the biomanufacturing workforce, particularly in the context of talent management. The study conducted a quantitative comparison of talent supply to talent demand at both macro and micro levels, focusing on Biopharma 4.0 technology clusters, including Automation, Digitisation, Data Science, Cyber, and Sensors. This analysis revealed a nuanced picture, with mature talent in automation, cybersecurity, and sensors, but a potential skills gap in emerging areas such as agile methods, virtual reality, AI, and digital twins.

Exploring the Machine learning skills gap at a more ‘granular’ level, Figure 1 details current skills gap in Machine Learning and Artificial Intelligence more specifically. The main current talent deficit is illustrated with Support Vector machine (SVM) and Random Forrest (RF) expertise. SVMs are machine learning algorithms that can be applied in classification of microorganisms, protein sequence classification, predicting protein-protein interactions, Quality, and fault detection systems. These are a few examples of how Support Vector Machines can be utilized in biomanufacturing, providing a robust framework for classification and prediction tasks. Random Forest is a machine learning algorithm that combines multiple decision trees to make predictions or classifications. It can be applied in many ways in biomanufacturing. The algorithm's ability to handle complex datasets, identify important features, and make accurate predictions makes it a valuable tool in various aspects of biomanufacturing processes such as Cell Classification, Quality Control of Raw Materials and Process Parameter Optimization.

References

• Allenby, M.C. and Woodruff, M.A., 2022. Image analyses for engineering advanced tissue biomanufacturing processes.?Biomaterials,?284, p.121514.
• Joshi, H. and Patel, J., 2023. A Literature Review on Emerging Trends in Adopting Industry 4.0 within Pharmaceutical Industry.?Technology, Agility and Transformation: Emergent Business Practices, p.115.
• Pokhriyal, P., Chavda, V.P. and Pathak, M., 2023. Future Prospects and Challenges in the Implementation of AI and ML in Pharma Sector.?Bioinformatics Tools for Pharmaceutical Drug Product Development, pp.401-416.
• Rathore, A.S., Thakur, G. and Kateja, N., 2023. Continuous integrated manufacturing for biopharmaceuticals: A new paradigm or an empty promise??Biotechnology and Bioengineering,?120(2), pp.333-351.
• Sarmadi, A., Hassanzadeganroudsari, M. and Soltani, M., 2023. Artificial Intelligence and Machine Learning Applications in Vaccine Development.?Bioinformatics Tools for Pharmaceutical Drug Product Development, pp.233-253.

About Evolution

A specialised Global recruitment company providing Biopharma talent for over 20 years. Dedicated network experts who recognise the critical value of talent as intellectual assets directly correlated to business success.

As global leaders in Biopharma Talent Intelligence TM, Evolution work with University of Dundee conducting novel research into Biopharma Talent Dynamics, Skills Gaps, Training requirement s and cost of recruitment models