Sapian is Kantify's AI-based drug discovery platform

Limb-girdle muscular dystrophies (LGMD) are a group of rare genetic muscle disorders. LGMD R2, a subtype of LGMDs, is caused by a loss of the function of dysferlin, a protein that plays a key role in repair mechanisms in skeletal muscles. Patients affected by this disease will generally progressively lose mobility throughout their lives. Like most neuromuscular disorders, no approved treatment exists today.

In an effort to advance the understanding of the disease, we used Sapian.Target to identify multiple novel, druggable targets for LGMD R2. We validated the potential of these targets in multiple ways: firstly, by silencing the targets (using siRNAs) in a cellular model of the disease (based on induced pluripotent stem cells - iPSCs), we showed that LGMD R2 cells were particularly sensitive to the presence of these targets to maintain their structural integrity. Secondly, we identified that multiple repurposable drug candidates for LGMD R2 are dependent on the presence of these targets to induce their positive effect - further validating the targets' importance in treating LGMD R2, and also demonstrating novel Mechanisms of Action (MoA) through which repurposable compounds act to induce their effect.

This work has opened up the road to develop targeted therapies for LGMD patients. Furthermore, we can now start the work to identify other disorders that could be treated using this MoA - resulting in a potential far beyond individual rare diseases.

Metastatic Castration-Resistant Prostate Cancer (MCRPC) are aggressive cancers that arise in response to first-line hormone therapy in Prostate Cancer. Unlike Prostate Cancer, which generally has a relatively good prognosis, MCRPC are extremely aggressive cancers, which very high recurrence rates and high tendency to further metastize. MCRPC does not have adequate treatment, meaning most patients diagnosed with this type of cancer have very poor outcomes.

In an effort to advance treatment of MCRPC, we used Sapian.Hit to identify New Chemical Entities that acted on novel targets we had previously identified. We virtually screened close to 10 million compounds in order to shortlist 3 candidates for wet-lab testing. Two compounds showed significant target engagement and significantly reduced proliferation in cellular assays. Our best compound was tested in vivo, where it induced full growth arrest on tumors, without exhibiting any notable toxicity.

This drug is currently undergoing further development in order to advance towards human trials.

Cardiotoxicity is a common cause for failure of drugs, with estimates ranging from 5%-15% of drug pipeline failing because of unacceptable heart toxicity. Often, this toxicity is caused by a drug inhibiting the function of a particular protein called Kv11.1, encoded by the hERG gene. Drugs that interact with this protein can cause various arrythmias or cardiac arrest, and sadly, it requires specialized assays or advanced animal tests to uncover this toxicity, often abbreviated in hERG toxicity.

We used Sapian.ward to detect whether we could differentiate between small molecules that selectively interacted with multiple promising targets that are closely related to hERG, and those that inhibited both hERG and our target of interest. We tested over 200 compounds from different chemical scaffolds, and purposefully included an equal amount of likely hERG-toxic compounds to be able to validate that experimental results matched with our predictions.

We showed that we achieve a near 95% (balanced) accuracy in predicting hERG toxicity on unseen compounds with very high chemical diversity, further validating that we can indeed identify, and filter out, compounds that cause this major issue in drug development.