How AI Is Revealing New Targets for Cancer Drugs


Targeted drug therapy has proven to be a highly advantageous approach to cancer treatments, presenting high efficiency and low patient drug resistance. 

Yet, there are drawbacks to the use of targeted drug therapy for cancer, including a lack of identified druggable genomic targets that extend across the patient population. Artificial intelligence algorithms can help researchers better understand carcinogenesis and identify new cancer targets. 

Artificial Intelligence (AI) is a field combining computer science and extensive data sets to perceive, understand, and solve problems.

Two major branches of AI applied biologically are machine learning-based and network-based. Machine learning is an application of AI wherein a computer can learn without direct instruction through mathematical models and pattern recognition. Network-based AI sorts and compares data, providing and compensating different perspectives. 

The Food and Drug Administration (FDA) and the international community have presented an increased interest in trustworthy and ethical AI adoption and innovation.


The development of multiomics technology is a factor that has bolstered the process of identifying novel anti-cancer targets. Multiomics is an approach to biological analysis that consists of forming data sets out of “omics”, which in the case of cancer are epigenetics, genomics, proteomics, and metabolomics. AI can analyze these data sets to investigate for anti-cancer targets. 

BioRender image by You Et al. Artificial intelligence in cancer target identification and drug discovery.


Unlike the changes in the nucleotide sequence characteristic of genetic mutation, epigenetics is similar to an “on-and-off” switch for the expression of specific genetic attributes without altering the genome. Cancer can be caused by both a genetic mutation or an epigenetic signal gone awry. 

A major problem facing epigenetics cancer research is finding specific gene patterns that predict which patients will respond to a cancer treatment. The study of the reversal of epigenetic modifications through AI can give insight into how exactly healthy cells become cancerous and which genetic marker within a patient will respond to cancer-treating drugs.


The study of genomics involves the mapping of an entire genome including its structure, function, and evolution, through genome-wide assays such as sequencing. A network-based AI is capable of finding similarities in specific genetic sequences and patterns in their phenotypic expression and interactions. Cancerous biomarkers can be identified through genomic data sets, identifying which genes medical professionals should consider oncogenes of interest.


Proteomics refers to proteins and their interactions within the body. Protein-protein interaction (PPI) research classifies a certain type of protein as “indispensable” and associates them as a major site of disease-causing mutations and drug targeting. In a 2017 study, Ravindran et al. found by analyzing the human PPI data from cancer patients that there are 56 indispensable genes in nine cancers, 46 of which were associated with getting cancer for the first time. This protein interaction data can be harnessed through AI to reveal novel cancer-associated interactions and their potential drug targets. 


Metabolomics is the study of metabolites: the substrates, intermediates, and products of the human metabolic pathway. A hallmark of cancer cells is that they work to alter these metabolites in order to support their rapid growth, providing energy to their biosynthetic pathways and changing their redox balance. Cancer can be detected through biological AI network analysis due to the presence of certain biomarker metabolites within biofluids, cells, and tissues of the body.


All aforementioned “omics” data can be presented for review in the form of multiomics integration analysis. Varied and interconnected data in a network format allows researchers to study carcinogenesis and drug targeting from an overarching perspective in a multifaceted group of patients. 

While treatment of cancer remains a formidable challenge, rapid technological advances in data collection and analysis through the use of artificial intelligence combined with robust information exchange may prove to be increasingly beneficial to the way cancer drugs are created and tested, leading to potential a safer and healthier world.

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Biomedical Research Genetics

First CRISPR-based Gene Therapy Could be Approved in 2023

CRISPR-based gene therapies have yet to be approved by the FDA, despite their relative affordability and ease when compared to traditional gene therapies. This may change in 2023, as CRISPR Therapeutics and Vertex Pharmaceuticals announced that their biologics licensing applications (BLAs) to the U.S. Food and Drug Administration (FDA) were completed, including a request for priority review, which would shorten the FDA’s traditional twelve-month review of the application to eight months. This timeline opens the possibility for the first CRISPR gene-edited therapy to be available for interstate commerce within the year.


CRISPR/Cas9 complexes were initially discovered in the natural immune systems of bacteria to protect them from viral invaders. The CRISPR component is a sequence complementary to a specific “target” sequence in a patient’s genome. It is sometimes referred to as the Guide RNA, as it guides the entire complex to the place within the genome where editing will occur. The Cas9 enzyme is the protein commonly depicted as a pair of metaphorical scissors, as it cuts DNA to allow for the insertion/deletion of intended genetic material. In the medical field, CRISPR genetic editing can be harnessed to potentially edit the genomes of individuals affected by currently incurable genetic diseases.

The CRISPR/Cas9 complex binds to and cuts the target sequence. / Javier Zarracina via

CRISPR/Cas9 is much more accessible than other FDA-approved gene therapies, due to its relative affordability and ease of use. Yet the FDA has shown caution when it comes to its approval. Major limitations of CRISPR-based medicines include:

  • The potential for off-targeting, wherein the complex incorrectly recognizes and binds to a sequence similar to the target sequence.
  • The triggering of the body’s immune response by CRISPR/Cas9, as it originates from bacteria.
  • The multi-faceted ethical concerns that come with genetic editing.

Methods and results

Despite concerns, researchers with CRISPR Therapeutics and Vertex Pharmaceuticals are in the final stages of clinical trials and are up for FDA approval with their CRISPR/Cas9 therapy for genetic blood disorders, called exagamglogene autotemcel (exa-cel).

Patients with sickle cell disease (SCD) and transfusion-dependent beta-thalassemia (TDT) who are participating in these trials have stem cells collected from their own blood. These cells are then edited with CRISPR/Cas9 outside of the body. Once the edited cells are introduced back into the body the patients are treated in accordance with traditional hematopoietic stem cell transplant (HSCT) procedures to establish high levels of fetal hemoglobin (HbF) production. HbF is the protein that carries oxygen throughout the bloodstream during fetal development.

The addition of HbF to a patient with SCD allows for a reduction or potential elimination of vaso-occlusive crises, wherein sickled red blood cells block blood flow to specific tissues, depriving them of oxygen and triggering an extremely painful immune response.

Within patients with TDT, increased levels of HbF reduce or eliminate the life-long dependence on blood transfusions that come with the characteristic severe anemia of the disease.

A doctor drawing blood from a patient. / Nguyen Hiep via

CRISPR Therapeutics and Vertex Pharmaceuticals are in stage III of clinical trials, assessing both adults and children with SCD/TDT. They presented the adult data from 75 patients (31 with SCD and 44 with TDT) at the European Hematology Association Congress in December 2022.

All of the 31 patients with severe SCD that had been experiencing recurrent vaso-occlusive crises saw an elimination of the crises at their follow-up after exa-cel infusion (follow-up ranging from 2.0 to 32.3 months).

Of the 44 blood-transfusion-dependent patients with TDT, 42 were transfusion-free after exa-cel infusion (follow-up ranging from 1.2 to 37.2 months) and two were at the 75% and 85% marks in transfusion-reduction.

These CRISPR-based therapies show solid potential to change the idea of “incurable” blood diseases.

This research supports the biologics licensing applications (BLA) of CRISPR Therapeutics and Vertex Pharmaceuticals. A BLA is a request to the FDA to introduce a biological product, in this case the exa-cel gene therapy, to the interstate market. Within the BLA, there is a request for Priority Review, which would shorten the FDA’s traditional twelve-month review of the application to eight months, potentially allowing for the first CRISPR gene therapy to be FDA-approved within 2023.

Although the exa-cel CRISPR gene therapy is not approved just yet, it is an exciting innovation for CRISPR research and patients affected by “incurable” genetic diseases.

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  • Kingwell, K. (2023, April 3). First CRISPR therapy seeks landmark approval. Nature News.
  • Ran, F. A., Hsu, P. D., Wright, J., Agarwala, V., Scott, D. A., & Zhang, F. (2013). Genome engineering using the CRISPR-cas9 system. Nature Protocols, 8(11), 2281–2308.
  • Vertex and CRISPR therapeutics complete submission of rolling biologics license applications (Blas) to the US FDA for exa-Cel for the treatment of sickle cell disease and transfusion-dependent beta thalassemia. Vertex Pharmaceuticals. (2023, April 3).
  • Vertex and CRISPR therapeutics present new data on more patients with longer follow-up treated with exagamglogene autotemcel (exa-cel) at the 2022 European Hematology Association (EHA) Congress. Vertex Pharmaceuticals. (2022, June 11).