The pharmaceutical industry is currently undergoing a significant transformation. More competition, more data, more regulations, and the need to stay ahead in a costly and competitive market are all putting pressure on the industry to move faster than ever before. Artificial intelligence-powered pipeline analysis is emerging as a tool that can help teams make better, faster drug development decisions. To explore these changes in the industry and how AI can address this, the Vibe Bio team ran a webinar in mid-December. Let’s walk through the big takeaways from the webinar.
Challenges in modern drug development
It’s clear that biopharma is striving for efficiency, seeking to streamline resources, increase the pace of drug development, and ensure performance improvements in the year ahead. One of the key components of the drive for efficiency is trimming costs, unsurprisingly. In 2023, 187 biopharmaceutical companies implemented layoffs, reflecting the industry’s push for greater efficiency. That trend continued in 2024, rising to 192 layoffs, impacting tens to hundreds of thousands of jobs. These layoffs impacted companies of all sizes, including three big pharma companies: Bristol Myers Squibb, Pfizer, and Johnson & Johnson all recorded multiple workforce reductions per year for both these years.
In addition to layoffs, the industry has also seen a huge push to move faster. By moving faster, many companies can meet the potential valuation and revenue expectations from public and private markets, bringing forward a lot of the value in their drug programs sooner. In the context of drug development, where the average Phase III (Ph3) clinical trial takes approximately two and a half years, it is becoming clear that organizations are eager to make investments in technologies and capabilities that allow them to advance their medicines through the development phases more rapidly.
Today, markets have stabilized a bit more, and many biopharma companies are looking for ways to leverage existing resources but also be able to add capacity and do more, faster. While every organization has different workflows, the common thread tying everything together is the importance of evaluating the quality and potential of the science of a specific program or a broader pipeline. The process of evaluating these assets has historically been highly analog and extremely complex. It required a multitude of different experts and stakeholders at the table to identify the right data, collect it, and bring it all together. At that point, teams still need to sift through all of the noise to find the right piece of information for a particular analysis and evaluation.
The complexity of the science, along with the analog nature by which experts ascertain this information and judge the potential of these assets, has historically been a huge bottleneck. The use of AI has been around in biotech for quite a while, but typically, it has been siloed to a specific use case or workflow or used more broadly across the enterprise. What is now emerging is a new opportunity: AI can be leveraged in life sciences and pharma by sitting on top of the full suite of drug development activities to provide an integrated, holistic analysis of existing assets and portfolios to support strategic decision-making.
AI revolutionizes drug development decision-making
If leveraged appropriately, AI can supercharge pharmaceutical companies’ efficiency by:
- Structuring, parsing, and analyzing data quickly from a wide variety of different disciplines simultaneously, gathering insights without having to leverage and burden in-house teams
- Allowing perspective focus because AI enables each organization to hone in on the most impactful risks and opportunities across their drug development pipeline
- Analyzing all relevant data without biases, unlike individuals who have only their individual memories and the data available at their fingertips to make their decisions
In addition to these benefits, AI also provides an opportunity to improve an organization’s resourcefulness because it allows teams to generate insights and analyses on demand rather than relying on a single point-in-time analysis. AI also enables biotechs to make strategic decisions at every stage of the drug pipeline process across assets and pipelines.
use cases for AI that best align with biotech needs include competitive landscaping, business development and opportunity assessment, portfolio prioritization, and identifying new opportunities and domains to enter.
Building AI-enabled teams
With the right technology, it’s now possible to build truly AI-enabled teams, allowing the industry to tackle many of the historical challenges around data and aggregation. It also brings existing staff, subject matter experts (SMEs), and AI together to generate sophisticated analyses and customize the insights generated based on the unique capabilities and interests of a particular organization and company.
To maximize the success of your drug portfolio program, organizations must take a collaborative approach between the humans and the right AI solutions. Each of the components of the drug development process bring different strengths to the table.
- In-house experts and SMEs are great at identifying the strategy of the organization, its strengths, and its potential weaknesses. This helps set the focus of the AI analysis project as well as the bounds for what the organization is trying to achieve. It also provides a layer of judgment and curation such that the results and the analysis being done are relevant to the organization with the right data sets and can be integrated easily into the broader strategy.
- AI-powered analysis complements the expertise of individuals and humans perfectly due to its ability to rapidly sift through large quantities of data, ingest it, and produce meaningful insights on demand, and its ability to eliminate the human biases that often emerge in team-oriented analyses. It also provides the flexibility to move from indication to indication or modality to modality with ease.
There’s a unique opportunity for AI to provide a rigorous, detailed, and scalable way to analyze a wide variety of scientific data at scale and analyze it to support broader workflows in collaboration with humans and experts.
Watch this webinar on demand, “How AI analysis supports drug development,” to learn more and see a demo of VibeOne in action.
