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.

Figure 1 illustrates current skills gap in Multivariate Statistical Data Analysis (MVDA). Skills gap apparent specific to Process Monitoring and Control techniques such as Principal Component Analysis (PCA) and Partial Least Squares (PLS). As MVDA techniques play a crucial role in biomanufacturing by providing a comprehensive approach to analyse complex multivariate datasets, the potential skills gap may influence process monitoring, quality assessment, process optimization, and facilitate decision-making in various stages of biomanufacturing.

Figure 1, shows a positive trend demonstrating that ML and MVDA are becoming more utilised in the Biopharma sector. The utilisation of ML within the last five years has gradually increased by approximately 40% per year since 2018. The rise in the application of these techniques is most likely due to an increase in the size and complexity of data available for Biomanufacturing analysis, which in turn, requires more advanced algorithms to analyse these complex data sets.

Additionally, due to access to high-performance machines and the recent advances in computational power, the ability to train algorithms on large complex data sets is now possible. The increased utilisation of both MVDA and ML algorithms within the Biopharma industry is likely to persist and accelerate as the sector further adopts these algorithms for better decision-making within clinical and commercial manufacturing. The skills gap illustrated in ML and MVDA, refer to the mismatch between the demand for skills and the available talent with the necessary expertise. As the applications of ML and MVDA continue to expand across industries, there is a growing need for professionals who can effectively apply these techniques and extract valuable insights from complex datasets. However, as illustrated, there is currently a shortage of individuals with the required skills and knowledge to meet this demand.

As a summary, the key findings of the ML and MVDA skills gap include technical expertise and domain knowledge. Technical expertise skills gap, distinctive to ML and MVDA, involves working with advanced algorithms, statistical methods, and data analysis techniques. Proficiency in programming languages such as Python, R, or MATLAB, as well as knowledge of relevant libraries and frameworks (e.g., scikit-learn, TensorFlow, or PyTorch), also needs increasing.

Current skills gap in Machine Learning and Artificial Intelligence more specifically relates to Support Vector machine (SVM) and Random Forrest (RF) expertise, and with Multivariate Data Analysis, skills gap is apparent specific to Process Monitoring and Control techniques such as Principal Component Analysis (PCA) and Partial Least Squares (PLS). Addressing the ML and MVDA skills gap requires a multi-faceted approach.

This includes initiatives such as educational programs that provide comprehensive training in ML and MVDA, collaborations between academia and industry to bridge the gap between theoretical knowledge and practical applications, and continuous professional development opportunities to keep up with the evolving techniques and technologies in these fields.

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