11 Biopharma Trends to Watch in 2024

Organoid Intelligence

In February, scientists founded a new field: “organoid intelligence” (OI), which I consider one of the potentially most impactful ideas in the biological sciences—for the better or worse.

Led by Dr. Thomas Hartung in the U.S., they are developing biocomputers using brain organoids—lab-grown tissues mimicking organ functions—from human stem cells.

These brain organoids, though not structurally identical to human brains, exhibit neuron-like functions and are envisioned to surpass the computational efficiency of supercomputers, offering novel approaches in pharmaceutical testing and insights into brain functioning.

The field confronts technological challenges like scaling up organoids and developing brain-computer interfaces for data exchange. It also confronts ethical considerations regarding the potential consciousness and rights of these organoids, necessitating a rigorous and inclusive ethical framework for development.

We are in the early days. Thankfully! Why? While I do not believe LLMs or other in silico artificial intelligence can become dangerous AGI any time soon, I am certainly less sure about that when we talk about actual biology-based systems combined with hardware, digital interfaces, and data. These little cyborgs are frightening and exciting at the same time!

A mixed year for AI in drug discovery

2023 has been a pivotal year for reconsidering the role of AI in early-stage and preclinical drug discovery.

On the one hand, we have distinct success stories, like those of Insilico Medicine, a company that managed to build a diversified clinical pipeline, including a recent start of Phase 2 for a drug candidate to treat idiopathic pulmonary fibrosis (IPF), five phase 1 candidates for various indications, including kidney fibrosis, inflammatory bowl disease (IBD), immuno-oncology, and COVID-19, and around a dozen preclinical programs in late stages of development — all within 3-4 years since the start of internal pipeline. What is more striking is that the majority of programs are based on actually novel targets, discovered by company’s PandaOmics system, which is a multimodal AI platform for target discovery. 

Another notable example which I would count as a 2023’s AI success is that Verge Genomics got positive safety and tolerability data from the Phase 1 clinical trial for its leading candidate VRG50635, a potential best-in-class therapeutic for all forms of ALS. Verge Genomics used CONVERGE™, the company’s all-in-human, AI-powered platform to develop its drug discovery program.

Also, the FDA's clearance of A2A Pharma's Investigational New Drug (IND) application for A2A-252, a TACC3 protein-protein interaction (PPI) inhibitor, showcases the potential of AI in accelerating drug development. Utilizing its AI-driven SCULPT computational platform, A2A Pharma, with a lean team of four and limited funding, managed to advance two clinical stage programs, including A2A-252.

Finally, there were successes in drug repurposing and indication expansion, for instance, by Dallas-based clinical stage company Lantern Pharma, a developer of RADR®, or “Response Algorithm for Drug Positioning & Rescue”, AI platform. The company is applying its biomarker discovery and drug design platform to finding novel indications, drug combinations, and likely-responder patient groups among broader patient populations, allowing for more cost-efficient and robust clinical trials, as well as personalized therapies. The company’s pipeline includes two Phase 2 candidates, five Phase 1 candidates, and a number of preclinical programs in oncology and CNS diseases. Notably, using their novel, highly accurate AI algorithm, Lantern Pharma managed to predict blood brain barrier (BBB) permeability with an impressive 89–92% accuracy, offering a rapid, cost-effective way to screen drugs or compounds to determine their potential to cross the BBB. 

On the other hand, in 2023, we have witnessed a number of clinical trial setbacks for some AI-designed drug candidates, including Exscientia's cancer drug candidate EXS-21546 which did not reach target results in Phase 1/2. 

An AI-inspired schizophrenia drug candidate from partners Sumitomo Pharma and Otsuka Pharmaceutical failed to outperform a placebo in two Phase 3 studies. Sunovion, a subsidiary of Sumitomo Pharma, brought compounds to the alliance that were then screened using PsychoGenics’s SmartCube technology, which employs computer vision to analyze and define the behaviors of mice treated with a potential drug.

Adding to negative statistics, BenevolentAI's lead drug BEN-2293 failed to beat a placebo in a Phase 2a atopic dermatitis study, leading to cutting up to 180 jobs and reorganizing its pipeline to conserve cash.

Despite turbulent drug candidate progress dynamics, 2023 was very productive for the AI adoption by the pharma industry, including numerous academic breakthroughs, technology launches, like NVIDIA’s introduction of the BioNeMo generative AI platform for biology research, and research pilots to implement so-called AI foundation models for large-scale omics projects, like in the case of Ginkgo Bioworks parntership with Google Cloud.

For a deep dive into AI progress in drug discovery and biotech, as well as 2023 briefings, refer to BiopharmaTrend’s landmark report, “The State of AI in the Biopharma Industry.“

A great year for AI in clinical research

In contrast to early drug discovery and preclinical trials, where data bottlenecks are still a major roadblock for swift AI adoption, the field of clinical trials is advancing much more smoothly.

Big Pharma hints that AI is already impacting clinical research. According to a 2023 Reuters report co-authored by Natalie Grover, Martin Coulter, and Julie Steenhuysen, Amgen's AI tool, ATOMIC, now scans vast data to rank clinics and doctors based on recruitment history, cutting enrollment time for some mid-stage trials by half. By utilizing ATOMIC, Amgen aims to shorten the typical drug development timeline by two years by 2030.

Novartis also leverages AI to expedite patient enrollment in trials, making the process faster, cheaper, and more efficient. However, AI is only as good as the data it is trained on. With only about 25% of healthcare data available globally for research purposes, there are still limitations.

Bayer utilized AI to decrease participant numbers in a late-stage trial for Asundexian. Specifically, it used AI to bridge mid-stage trial findings with extensive real-world data from millions of patients across Finland and the US, facilitating the forecasting of long-term risks among a population analogous to the trial. Bayer plans to use real-world data for an external control arm in a pediatric study of the same drug.

According to the Reuters report, Dr. Blythe Adamson, a senior principal scientist at Roche subsidiary Flatiron Health, emphasized how AI enables rapid and large-scale analysis of real-world patient data, contrasting it with traditional methods, which could take months to analyze data from 5,000 patients, whereas now millions of patients' data can be analyzed in just a few days.