The biopharma industry has been notoriously cautious and slow to adopt new technologies, not least because it is a strictly regulated space and the sector has built up long-standing, well-established processes to ensure safety and compliance. While some smaller, newer biotech companies have the agility to implement technologies powered by artificial intelligence and big data, such adoption is not yet widespread within the industry as a whole.
However, there is one area in which we are seeing a substantial amount of investment and innovation: predictive modelling with machine learning, a form of artificial intelligence (AI). This new direction has been influenced by the convergence of several trends.
Firstly, the digitalisation of information in healthcare and biopharma has advanced in recent years, with biopharma companies now making progress in curating an interoperable data backbone of rich, integrated, contextualised data that supports the development continuum. Secondly, the arrival of Big Tech has brought increased attention, talent and technology to the industry. And lastly, the emergence of the first clinical-use cases have made the industry feel more comfortable with the concept of AI and its use through the drug discovery and development life cycle.
The power of predictive
In early discovery, predictive modelling powered by machine learning and data science could help to predict the behaviour and interactions of various drug candidates, such as their biochemical characteristics, therapeutic effects and safety profiles.
Possible challenges and liabilities of the active component and formulation can also be predicted, enabling the maximisation of the compound’s developability and the ‘engineering out’ of off-target drug effects. Ultimately, these machine learning tools will better inform which drug candidates have the highest potential for success with regards to efficacy and developability.
In process development, machine learning can be applied to predict how well drug candidates will perform in cell culture, or define what the chromatography parameters should be. AI-powered tools and predictive modelling will soon be able to find the optimum conditions to run a unit operation with more certainty and fewer empirical experiments, increasing the robustness and speed of the process.
In manufacturing, predictive modelling will enable informed decisions about the infrastructure that will be needed to produce at scale – from what equipment will be needed and how a facility might need to be configured, to supporting Quality by Design and getting ahead of potential quality control issues. It could help save batches by allowing scientists to react to deviation trends or problems before they become detrimental to the batch.
Implementing AI in biopharma and healthcare
But the first crucial step in unlocking the power of predictive modelling and machine learning is the generation of a robust and well-maintained data backbone. No matter how each company achieves this, they must each build in a high level of interoperability, not only to facilitate the transition from siloed to streamlined data management within various company functions but more broadly to facilitate the common usage and interrogation of data within the industry. Such data federation requires the aggregation of data from a myriad of sources and the integration of this data into a common model.
Achieving this integration at an industry-wide level is challenging. The complex landscape of equipment and instruments leads to inaccessible data silos with important context and process knowledge locked away in paper records and spreadsheets. To overcome this, leading organisations are forging relationships with science and technology companies and embracing new innovations to help accelerate their digital transformation and realise the potential of AI and Biopharma 4.0.
IDBS, one of the Danaher operating companies, has recently launched the world’s first biopharma life cycle management (BPLM) platform, a new cloud-based data management platform that enables biopharma companies to more efficiently design, scale and run their processes. An embedded integration layer simplifies the curation of a process data backbone that powers data analytics and machine learning. By eliminating repetitive manual tasks, users are able to focus on developing more robust and scalable processes, supported by easily accessible data and actionable insights that accelerate process optimisation, technology transfer and regulatory filings.
Dedicated solutions are also being developed to help overcome specific challenges in the biopharma life cycle. For example, predictive modelling can be leveraged to scale up bioproduction processes, and perform fast, efficient and affordable experiments in silico. Another Danaher operating company, Cytiva, has created a digital bioreactor scaling tool, which allows users to scale bioreactor processes from bench to the Extended Detection and Response (XDR) platform and vice versa with a high degree of accuracy. The Cytiva Digital Bioreactor Scaler takes into account process parameters, along with bioreactor and cell line characteristics, and will propose options for scaling from process development to manufacturing scale.
We believe that in silico simulations will play a huge role in the future of bioprocessing. Earlier this year Cytiva acquired German scientific software manufacturer GoSilico, whose technologies create predictions to assist in the development of downstream chromatography purification processes. Process development is an intense and time-consuming part of making any therapy. By using the Cytiva Digital Bioreactor Scaler and mechanistic models to test different options for upstream and downstream processes, drug developers can expect to predictably manage resources, time and risk, and help in process understanding across the organisation. This sort of digital innovation will provide transformational capabilities for our industry, at a time when speed to market is more important than ever.
Broader applications of these groundbreaking technologies are going to radically disrupt the industry, all the way from the laboratory bench to point of care. The amount and complexity of health data at point of care is growing at an exponential rate, with increasing adoption of electronic health records (EHRs), wearable health apps and trackers that collect long-term personalised data, and services such as direct-to-consumer genetic testing that can be integrated into social networks. Meanwhile, machine learning is already being applied to quickly and accurately analyse hundreds of radiological images, enabling precise decisions as reliable as those made by trained and experienced physicians.
As digitalisation becomes increasingly fundamental to healthcare, it is imperative that the biopharma industry also does everything it can to embrace these disruptive technologies so we can all move towards a more personalised and precise approach to medicine. R&D experiments performed in silico are providing new insights into health and disease biology, revealing novel treatment targets, and providing new ways to precisely hit those targets. These digital experiments can also analyse huge data sets to identify novel biomarkers for delivering personalised therapy: ensuring that the right patient is treated with the right drug at the right time.
Building on federated data in robust data backbones, predictive modelling with machine learning is what will move our industry away from the inefficient empirical approach currently used in drug discovery and process development. Instead of carrying out hundreds of time-consuming, costly and inefficient ‘wet bench’ experiments to identify what succeeds, we will first experiment in silico by using highly trained machine learning-based models to quickly and accurately predict the outcomes.
Positive outcomes will then be tested in a few select experiments, which will also test the validity and utility of the digital models. Validated models and experimental data will then inform decision-making.
Through predictive modelling and beyond, using machine learning and other forms of AI and data science, we will be able to push the boundaries of what can be achieved by the biopharma industry. It will require investment, innovation and a mindset shift, but will enable us to deliver better medicines, faster, more accessibly, and with greater personalisation towards patients.
To continue reading this article please go to PharmaTimes .