How has CRISPR transformed therapeutic drug discovery?

CRISPR: screens & models

In 2013, the CRISPR-Cas9 system caused a wave of excitement in the scientific community as it emerged as a way to quickly, accurately, and efficiently genetically manipulate an organism's DNA. In comparison to traditional methods, such as TALONs and recombinant DNA approaches, CRISPR is reprogrammable – meaning that it can target new genes with ease [1–3].

CRISPR screens can be divided into two main types; pooled and arrayed. For both methods, perturbations (e.g. CRISPR knockout, base editing, or prime editing) and challenges (e.g. drugs) are introduced to the cells. The main difference is that arrayed CRISPR screening provides physical separation through wells in the laboratory plate, with different perturbations and challenges being introduced separately. With pooled CRISPR screens, the perturbations are introduced in bulk.

Pooled CRISPR screens have the advantage of being scaled up and are therefore predominately used for drug discovery. Arrayed CRISPR screens on the other hand can be combined with read-outs that do not require screening (e.g. proteomics and imaging) making them excellent for validation and follow-up studies [4].

There are also a few variants of CRISPR screening platforms; CRISPRn, CRISPRi, RNAi (miRNA-based shRNAs) and CRISPRa. From a drug discovery perspective, CRISPRi screening is ideal as it enables researchers to knock down gene expression, which closely mimics the effects of inhibitor-based drug mechanisms whilst transcription can be modified using CRISPRa [5]. A combination of these two methods, termed CRISPRi/a, allows for multiple genes to be manipulated, making it a great tool for studying cellular biology and the interactions between different genes [6].

Rich biological insights can be gained from pooled CRISPR screens when they are combined with sophisticated models. Human cell lines (such as tumor cells, adult cells, and stem cells) have dominated the field in being the most widely used biological model for CRISPR screens. Though in recent years, tissue explants, animal models and organoids have also caused a stir.

Organoids are 3D structures grown from stem cells in vitro that provide a prime platform for replicating key aspects of human pathophysiology and appraising the efficacy of multiple therapeutic agents on a per-patient basis [7,8].

Whilst traditional gene editing was limited to mice models, CRISPR-Cas9 systems have expanded the reach to rats, dogs, rhesus monkeys, and macaques [9–11]. The adaptability of CRISPR to animal models other than mice, has allowed researchers to study the effects of novel treatments in tissue that is more closely similar to human tissue in terms of structure, physiology, immunity, and metabolism [12].

Overall, CRISPR-based technology has incomparable advantages over traditional methods, such as high sensitivity and single-base specificity. In the following sections, the practical applications of CRISPR in drug development will be explored.

Applications of CRISPR in treating monogenic conditions & beyond

CRISPR technology has propelled drug development for several monogenic conditions such as sickle cell anaemia and transfusion-dependent β-thalassemia. So much so, that we are no longer discussing treatment outcomes for these conditions… but complete cures, which in the world of medicine, is like gold dust [13].

Another monogenic condition that has been the target of CRISPR gene editing is Huntington's disease, which is caused by a mutation in the Huntingtin gene. Researchers from bit.bio (Cambridge, UK) recently created a disease model for Huntington's using iPSCs, which had been generated utilizing CRISPR/Cas9-based gene editing. The model is an exact replication of the genetics of Huntington's disease, aiding the translation of research into the clinic [14]. Unfortunately, clinical trials for Huntington's disease have not come to fruition as of yet but preclinical studies are immensely advancing our understanding.

CRISPR-Cas9 technology has also been applied to drive forward drug development for HIV-1 (with the targeted gene being CCR5), neurofibromatosis-1 (targeted gene NF-1), and Parkinson's (LRRK2 gene) [19].

Furthermore, in a first-in-human IND, the US FDA approved using CRISPR-transactivator technology to treat a rare mutation, termed CRD-TMH-001, which causes Duchenne muscular dystrophy. This trial was followed closely by the rare disease community, however sadly the only patient involved died during the trial and the cause remains under review [15].

When considering the development of therapeutic targets for diseases caused by one gene, other aspects of the patients' genetics must also be taken into consideration. This is even more true for polygenic diseases, which are much more complicated to treat than monogenic ones as many additional factors are at play including gene-gene interactions, epigenetics, and environmental influences [16]. Prime editing and base editing are enabling researchers to manipulate multiple genes at once, however, the use of CRISPR in treating polygenic conditions lags behind the progress made for monogenic disorders [17].

Drug developers must also take into consideration the following questions: what are the combined effects of the numerous genetic mutations occurring in the same or nearby cells? Has the patient already received treatment? What stage of disease is the patient at?

To shift CRISPR from the lab to the bench, and achieve truly personalized medicine for more conditions, we need to be able to predict mutations on a patient-by-patient basis.

The adaptability of CRISPR to different animal models has also made it a very useful tool for studying infectious diseases as many human pathogens, such as influenza and tuberculosis, are best modelled in animals other than mice (for which traditional gene-editing tools were limited to). Furthermore, CRISPR–Cas3 and Cas-9 encoding phage's have the capability to identify drug-resistance genes (namely the AMR gene) in order to prevent the threat of antimicrobial resistance [18]. To date, CRISPR-Cas9 systems have been used to target HPV-16, the Epstein-Barr virus, HIV, Hepatitis B, the Herpes simplex virus, and the African Swine Fever Virus, predominately via transfection [19].

CRISPR as a tool for cancer drug screening

Mapping genetic interactions can uncover vulnerabilities in tumors and identify oncogenic drug targets [20]. One of the key advancements in cancer genomics was the development of a Cancer Dependency Map, to help elucidate the genetic workings of tumors. The project kick-started in 2019 when researchers from the Wellcome Sanger Institute (Cambridge, UK) and Open Targets performed genome-scale CRISPR–Cas9 screens to disrupt every gene in over 300 cancer models from 30 cancer types [21].

Since then, this team has developed a Project Score database, which utilizes CRISPR-Cas9 dropout screening data, across hundreds of cancer cell models, to identify genes key to cancer growth and metastasis. This freely available database enables users to identify and rank candidate drug targets on a scale from 0–100, on both a pan-cancer and a cancer type-specific level [22]. It forms a part of the Open Targets portal, a platform for the prioritization of drug targets in diseases [23]. Open Targets is also being combined with machine learning techniques to expedite the identification of new target-disease associations and create knowledge graphs, which provide a visual snapshot of the relationships between genes, diseases and drugs [24].

Other tools that have significantly advanced our understanding of genomics include the Cancer Cell Line Encyclopedia and the ENCyclopedia Of DNA Elements (ENCODE) [25,26]. By further understanding the complexities of the genomics of tumors, researchers have been able to push forward with uncovering the link between genetic variation and disease predisposition as well as the relationship between disease development and treatment response.

Practical applications of CRISPR in oncology

Using the CRISPR–Cas9 system, researchers have been able to carry out controlled gene editing of CAR-T cells; this has proved invaluable for hematologic malignancies by allowing scientists to modify immune cells to defeat resistance. To date, preclinical studies have been conducted using this technology on Ewing sarcoma, liver cancer, ovarian cancer, glioma cells, prostate cancer, acute lymphoblastic leukemia and B-cell lymphoma [27].

A key example of CRISPR technology being used is the trial by PACT Pharma Inc. and the University of California (both CA, USA) whereby a team of scientists reprogramed patients' immune system to target their own cancer by the knockout of two endogenous T cell receptor genes. The study involved 16 individuals who had already received standard treatment for their cancer (which included colon, head and neck, lung and skin) but whose cancers had returned. Out of the 16 participants, the personalized treatment resulted in 5 patients having stabilized disease. As with the CRISPR-based sickle cell anemia breakthrough, this immune-boosting therapy is a living drug, so one dose can theoretically be administered for lifelong protection [28].

The trickiness of rare variants

One of the key hurdles facing CRISPR is rare variants. Understanding rare variants is crucial to making advancements in genomic medicine as genetic data informs disease risk, and genetic variants guide therapies. The challenge in genomic medicine is that rare variants still prove tricky to interpret.

As testing becomes more widespread so does the number of VUS (a variation in a genetic sequence for which the association with disease risk is unclear) [29]. For example, BRAC1 – a gene that is synonymous with breast and ovarian cancer has had over 4000 variants reported, but over 50% of these variants are VUS [30].

So how can we evaluate variants for risk and avoid causing undue worry to patients undergoing genetic testing? Machine learning combined with genetic screening methods can provide a solution; computational models can help predict risk as they can interpret huge volumes of data. However, machine learning models are still not accurate enough to use on a patient basis. The most recent approach is the use of functional assays carried out in the lab, though, this method also suffers from limitations in terms of the quantity that can be processed and whether the results truly reflect the biological natural response. The trickiness of rare variants in pharmacogenomics is still a major challenge but it is hoped that the increasing ability to combine multiple genetic-editing tools could overcome this in the future.

There is also the elephant in the room - the ethics of gene editing. Thus far, scientists have tried to restrain the use of CRISPR to somatic mutations e.g. those that are not passed down through the next generation. However, cases such as the He Jiankui affair, involving the genetic modifications of embryos, have shown that this is not the stuff of science fiction [31]. As the origins of CRISPRs come from nature, it is a process that is relatively easily replicated with standard laboratory equipment, this ease of use proves problematic when trying to regulate its uses.

What's next for CRISPR in drug discovery?

The advent of CRISPR came with the excitement that it would revolutionize medicine. Our understanding of pharmacogenomics has advanced enormously with the development of genomic maps and innovative models that can be manipulated with CRISPR-Cas9, and a select number of monogenic diseases can now be cured using gene editing approaches.

This phenomenon of having a cure for a condition has caused a conundrum for the pharmaceutical industry, whose investment cynically depends on repeat orders. How do you price a drug that will completely cure a patient for life? Some pharma companies are looking at a staggeringly high upfront cost, that is partially refunded if the patient does not see long-term benefits. The ethics and regulations around drug pricing differ significantly across the globe, so we await to see how pharma responds to providing life-long solutions for patients.

On the side of polygenic diseases, there have been great strides forward with our understanding of the complexities of polygenic diseases, but further research is needed to reach the full potential of CRISPR in developing pharmaceuticals for these conditions.

Furthermore, how will the field of gene therapy evolve for conditions such as bipolar and depression, for which there are genetic dispositions and environmental factors, but also a substantial number of unknown causes.

Thus far, the genomic atlas has been fine-tuned and perfected, however, we are a long time away from widely achieving personalized medicine with these powerful genomic tools. A particular focus over the next few years will need to be the validation and quality assessment of gene-editing tools to measure whether CRISPR can live up to its momentous expectations.