trends pharma

Pharma industry trends 2022

Published by Tony Small

25 July, 2022

Innovation will continue to drive business growth

The global pharmaceutical manufacturing market was worth $402 billion in 2020 and yet the US and European markets alone are projected to reach $635 and $315 billion respectively by 2024 indicating some serious and dramatic transformations are in play within the industry right now.

A recent industry survey by Pharmaceutical Technology identified that 70% of the surveyed pharma clients expect drug development to be impacted by smart technologies such as Artificial Intelligence (AI), Machine Learning (ML) and Natural Language Processing (NLP). So let’s explore these trends in a little more depth.

Pharmacovigilance will gain scale and traction – there will be an increase in emphasis and investment in evaluating drug quality and optimising the benefit-risk ratio of healthcare products. This market segment is forecast to grow from $6,28 billion in 2021 to $14.85 billion in 2028, a CAGR of 13.1%. Pharmacovigilance requires companies to apply enhanced data mining tools and methodologies for critical decision-making and risk assessment to drive increases in the safety and efficacy of medicines.

Technology will drive R&D efficiency improvements – continuous digital transformation utilising Artificial Intelligence, Machine Learning, and cloud technologies will be used to lower the cost of R&D while enhancing efficiency and will be a key agenda for R&D operations.

Data analytics will accelerate biotechnology innovation – Machine Learning algorithms will reduce the cost and time to identify which drug formulations are more effective than others. Mining big data will help identify previously hidden patterns in clinical trials results and enhance drug development. Data analytics will help improve detection of early-stage disease and high risk patients; strengthen preventative care by better aligning treatment plans to the unique needs of patients; and offer enhanced modelling of disease spread and the development of enhanced mass disease prevention strategies.

Agility becomes a critical organisational capability – the industry’s transformation will require that people are quickly able to adapt to new ways of working and develop new skillsets required as the operating models evolve. People will need to be able to programme, operate and interpret data as well as keeping up-to-date with rapid technological advancements. Organisations are, and will continue to, adapt their operating models and structures to accelerate activities and decision making and to simplify and enhance client touchpoints.

Natural Language Processing (NLP) making sense from unstructured data – NLP has already started to help pharmaceutical companies create valuable insights from the mass of unstructured data generated by clinical trials. This trend is set to continue as NLP is growing faster than any other field of AI.

Digital tools will improve patient outcomes – digital tools such as telemedicine and remote working instruments (wearables, sensors and devices) will change the nature of patient interactions and make healthcare services more accessible. In addition, the use of digital tools will enable the pharmaceutical industry to leverage data in order to keep up with public health challenges.

CDMO market growth will increase market resilience – The Contract Development and Manufacturing Organisation model is already well established for existing drugs and is just starting to gain traction for the investigation of new drugs. CDMOs will find new market opportunities with a growing number o f small and medium-sized pharmaceutical companies which are responsible for an increasing share of new drug approvals but with no manufacturing capacity of their own.

Deep learning will transform adverse events monitoring – the forecast growth in the pharmacovigilance market together with an increase in the use of genetic screening will generate higher volumes of data to which deep learning can be applied to enhance anomaly detection and adverse event monitoring. Statistical methods can efficiently and correctly elicit and validate adverse events and exclude noise-induced ones.