This project is focused on the discovery and preclinical validation of first-in-class drug candidates for the treatment of Triple Negative Breast Cancer (TNBC) based on a novel approach to identifying novel genetic targets. TNBC is a high-risk malignancy with a high unmet need. TNBC grows and spreads faster than other invasive breast cancer subtypes, has fewer treatment options and a poorer prognosis. TNBC is traditionally considered the most difficult type of breast cancer to treat and patient outcomes are extremely poor relative to other subtypes. TNBC makes up around 15% of all breast cancers. In the UK around thousands of patients will be diagnosed with TNBC in 2023\. Incidence rates are high in women under 40\. The 5-year relative survival rate for patients where TNBC has spread to distant parts of the body (such as the lungs, liver, or bones) is 12%. Poor outcomes due to drug resistance and relapse are also common in patients with TNBC. This project will validate the results of a unique new AI-based bioinformatics platform being used to tackle the problem of highly genetically mediated cancers. The platform uses AI, machine learning and synthetic biology to advance new disease understanding, identify novel targets, and discover novel biologic drugs.

AMPLY Discovery, using its proprietary AI-based technology platform, has established a pipeline of new, naturally occurring bioactive molecules designed to target complex bacterial infections. AMPLY's drug candidates show strong in vitro efficacy and excellent toxicity and bioavailability profiles. This project is focused on the preclinical validation of best-in-class lead candidates as an effective treatment alternative for chronic and multi-drug resistant bacterial lower respiratory tract infections, particularly chronic infections in Cystic Fibrosis (CF) patients.

This project is focused on the discovery of first-in-class drug candidates for the treatment of Acute Myeloid Leukaemia (AML) based on a novel approach to identifying novel genetic targets. AML is a high-risk hematologic malignancy with a high unmet need. Limited treatment options exist, particularly for patients who are ineligible for intensive chemotherapy or experience relapse. AML is rare, making up around 1% of all cancers. In the UK around 3,100 patients will be diagnosed with AML in 2023 (CRUK). Incidence rates are typically higher for those over 45\. Mortality in older patients is high, around 2,700 patients in the UK will die annually from AML. The 5-year relative survival rate for people over twenty with AML is 28%. Poor outcomes due to drug resistance and relapse is common in patients with AML. This project will combine the unique AI-based bioinformatics platforms and expertise of two SMEs, based in the UK and Switzerland, to tackle the problem of AML using AI, machine learning and synthetic biology to advance new disease understanding, novel target identification, and novel drug discovery.

This project is focused on the discovery of best-in-class antimicrobial peptides (AMPs) and the preclinical validation of these new biologic molecules as an effective treatment alternative for multi-drug resistant tuberculosis (MDR-TB). Tuberculosis is a common bacterial infection, typically affecting the lungs, spread through inhalation of tiny droplets of infected matter. Historically the disease had a high mortality rate, however today the disease is considered imminently curable and preventable if treated with the right course of antibiotics. However, MDR-TB involves strains of the bacteria which are resistant to existing antibiotics and are therefore potentially not treatable. MDR-TB is a major emerging public health problem. In 2021, the WHO estimates that 450,000 people developed TB which was resistant to rifampicin (the first line TB drug), and there were 191,000 related deaths. MDR-TB is significantly more expensive and difficult to treat than standard drug-sensitive TB and its rise threatens the progress made in the twentieth century combating TB. An untreatable, uncontrolled MDR-TB epidemic could have huge health and economic implications.