3rd October 2023

Introduction

Worldwide the pharmaceutical industry is in crisis. Reliant on wet labs, the industry is stuck in ‘drug discovery 1.0’ - slow, expensive and outdated processes to deliver life-saving drugs to an aging and growing global population.

Worldwide the pharmaceutical industry is in crisis.

For pharma to succeed, urgent transformation is needed to speed up the entire drug development process. On average the process takes between 10-15 years; with between 5-7 years to identify and validate a target, and 3-7 years for clinical trials.

Drug discovery 2.0 - Lifebit’s solution for success

Drug Discovery 2.0 is the pharmaceutical industry’s opportunity to revolutionize pre-clinical drug development and clinical trials through federated data, analytics and AI. 

Data is pivotal in delivering fast and effective pre-clinical drug development. Moving from traditional processes to data-driven processes will accelerate discoveries and deliver faster, cost-effective, personalized medical treatments. 

Lifebit provides the three key elements of Drug Discovery 2.0:

1. Multi-modal federated data: Managing, linking and extracting insights from diverse data types across various sources and modalities (e.g. clinical, molecular, imaging). Implementing a democratized, user-friendly, no-code point-and-click solution tailored to drug discovery researchers for swift and accurate insights in a matter of a couple of days or weeks.

2. Data standardization: Seamless harmonization through automated tools to accelerate research capabilities

3. End-to-end analytical solutions: Moving away from isolated point solutions to provide comprehensive disease insights, ultimately aiding in target identification and verification.

Drug Discovery 2.0 is a shift away from traditional wet labs to data labs, which facilitate data science and experimentation, to accelerate target identification and validation to as little as a few months. In addition, data enables more accurate clinical trial design and recruitment with the possibility of doubling success rates and reducing time for trials from 6-7 years down to 3-4 years.

Drug Discovery 2.0 is the pharmaceutical industry’s opportunity to revolutionize pre-clinical drug development and clinical trials through federated data, analytics and AI.

Comparing traditional drug discovery processes with data-driven processes

Challenges with traditional drug discovery processes

The pharmaceutical industry is vital in providing life-saving therapeutics worldwide. However, the growing global population, coupled with the prevalence of chronic conditions, intensifies the demand for large-scale and fast clinical discovery and production. In turn, this is creating pressure for the pharmaceutical sector to deliver new medicine at pace.

The key challenges of traditional drug discovery processes are:

AI & Data Federation - the golden thread of the next generation drug discovery 2.0

Artificial intelligence (AI) promises a revolution for drug discovery, allowing computer systems and software to perform tasks that typically require human intelligence. While there have been success stories, investments in technology aimed at elevating data and analytics have not yet achieved the profound impact required by the pharmaceutical sector. 

Notably, some high-profile endeavors have not fulfilled their pioneering promises to transform health and life sciences. First generation data and technology solutions have fallen short; unable to support multi-modal data, end-to-end analytics, or secure accessibility for everyday researchers and unable to identify targets as quickly as needed in vital health research.

The introduction of federated technologies can overcome these challenges by enabling collaborative insights without needing to move or centralize the data, ensuring data privacy and security, which is crucial in the pharmaceutical industry. Federated technology is a software process that enables numerous databases to work together as one. 

Combining AI and federated technologies through federated machine learning in drug development processes can allow the safe linkage of distributed health datasets and the fast and accurate analysis of these data to achieve target results. This has the potential to reduce the time to access vital health data from up to 6 months down to just two weeks and generate results from the data in 2-4 weeks rather than 9-16 months.

This has the potential to reduce the time to access vital health data from up to 6 months down to just two weeks

Summary

Traditional processes for drug discovery are marked by slow pipelines, scientific complexity, and soaring costs. Overcoming these challenges requires Drug Discovery 2.0 - more effective use of data, next-generation technology to enable federation and data linkage, automations and cutting-edge AI tools such as Federated Machine Learning.

Without rapid transformation of processes and use of scalable innovative technologies for data and analytics, the pharmaceutical industry is at risk of failure and, more importantly, patients will miss out on life-saving treatments

About Lifebit

Lifebit provides federated data analysis services for clients, including Genomics England, Boehringer Ingelheim, Flatiron Health and more, to help researchers transform data into discoveries.

Interested in learning more about Lifebit’s data services and how we accelerate research insights for academia, healthcare and pharmaceutical companies worldwide?

Contact us  Request a demo