AI Helps Discover Potential First-in-class Molecule for Immuno-oncology for Fosun Pharma

by Andrii Buvailo, PhD          Biopharma insight

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The global artificial intelligence (AI) leader Insilico Medicine announced its first milestone in cancer immunotherapy for their external collaboration with Fosun Pharmaceuticals (600196.SH, 02196.HK), an international healthcare group in China.

In under 40 days since the collaboration announcement, companies nominated the first preclinical drug candidate ISM004-1057D, a potential first-in-class small molecule inhibitor that targets glutaminyl-peptide cyclotransferase-like protein (QPCTL). QPCTL is responsible for CD47 pyroglutamate formation, the modification essential for the interaction of CD47 with SIRPα. The small molecule inhibitors of QPCTL mimic the therapeutic effect of CD47 antibodies to attack tumor cells by blocking this signal.

Insilico Medicine and Fosun Pharma are working together on the IND-enabling studies to advance this program to clinical trials. This discovery triggered milestone payment for Insilico Medicine and opened doors to potential profit-sharing opportunities from the commercialization of the QPCTL program.

Targeting CD47-SIRPα to boost anti-tumor immunity

An important mechanism cancerous cells use to deceive the immune system is by overexpressing CD47, a signaling molecule that is widely regarded as a “don’t eat me” message for the macrophages and other immune cells. Normally, this signaling protein is used by healthy cells to protect against autoimmunity.  CD47 protein interacts with another important player in this process -- signal regulatory protein α (SIRPα), a membrane glycoprotein present on the surface of some immune cells (myeloid cells) -- leading to negative control of the innate immune cells.

The knowledge about how cancer cells use CD47-SIRPα interaction to hide from the immune system opens doors for promising new strategies to cure cancer. One powerful idea behind immunotherapy is to disrupt CD47 signaling in cancer cells to deprive them of their shield against immune attack.


Tackling challenges

A number of companies and research groups have attempted to inhibit CD47 glycoprotein using anti-CD47 antibodies. The problem with this approach -- leading to the discontinuation of several clinical trials in the past years -- is that CD47 signaling proteins are present not only on cancerous cells, but also ubiquitous among healthy cells, for instance, those of the hematopoietic system (normal red blood cells, senescent red blood cells, and platelets). This often leads to serious off-target effects, leading to anemia and other complications.

The so-called “antigen sink” effect could also pose a problem in the development of anti-CD47 antibody treatments. CD47 is expressed ubiquitously among malignant and healthy cells, meaning that large initiation doses and/or frequent administrations may be required for a drug to achieve an effective therapeutic effect. The alternative route is to therapeutically target SIRPα molecule, which is less distributed vs. CD47. SIRPα is highly expressed, however, on myeloid cells and central and peripheral nervous system cells, so the potential for neurological side-effects should be considered when using therapeutics that target SIRPα.

In contrast to that, the preclinical candidate compound ISM004-1057D discovered by Insilico Medicine and Fosun Pharmaceuticals is a small molecule inhibitor of QPCTL, so it could potentially provide not only high tumor penetration but also reduce hematologic side effects and avoid antigen sink. The molecule already showed in vivo anti-tumor efficacy in both liquid and solid tumors and demonstrated favorable pharmacokinetics and ideal safety profiles in vivo preclinical studies, with auspicious value in further clinical development.


Speed matters

In 2020, an estimated 19.2 million people worldwide were diagnosed with cancer, and almost 10 million people died the same year from this devastating cause -- around 1 of every 6 deaths worldwide, so speedy discovery of novel immunotherapy interventions is the global priority.

As is claimed in the press release, the novel drug candidate has been nominated by Insilico Medicine and Fosun Pharmaceuticals in just under 40 days since the collaboration announcement -- what looks like an industry record for speed. For example, a brief glance at several other AI-drug discovery programs suggests a significant acceleration in the speed of research in this case.

AI company Benevolent AI Exscientia Exscientia Insilico
Partner AstraZeneca GlaxoSmithKline Bristol Myers Squibb Fosun Pharma
Release date 2019-04-30 2017-07-01 2019-03-20 2022-01-12
Release link link link link link
First milestone date 2021-01-27 2019-04-03 2021-08-18 2022-02-16
Milestone link link link link link
Milestone Target identification Target to lead Hit to PCC Hit to PCC
Time 33 months 21 months 29 months 1.5 month


AI-enabled drug discovery

According to the company, rapid discovery of the novel QPCTL inhibitor has been enabled by Insilico Medicine’s “end-to-end” artificial intelligence (AI) platform, which includes target discovery system PandaOmics, and lead discovery system Chemistry42. The AI platform includes hundreds of models and components, including deep learning architectures, natural language models, and other algorithms. The AI platform helped develop the target hypothesis in oncology and generate compounds with good drug-like properties. After synthesizing and testing 71 compounds, the company managed to deliver preclinical candidate compounds within 9 months from project initiation.

Earlier, Insilico Medicine’s AI platform demonstrated a series of proof-of-concept milestones in various therapeutic areas – including Idiopathic Pulmonary Fibrosis (IPF) and Kidney Fibrosis (KF). For example, they managed to discover a novel pan-fibrotic target and nominate a corresponding drug candidate for IPF within 18 months and later nominated another drug candidate for KF just several months later. Recently, the IPF drug candidate entered first-in-human clinical trials in Australia.

Topics: Novel Therapeutics    Emerging Technologies   

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