Software, Synthesis and Screening: cheminformatic led invention, design and synthesis of novel compounds for the treatment of septic shock

Septic shock is a complication of infection that leads to dangerously low blood pressure. In the UK, sepsis affects >150,000 patients annually across the age range, and death rates are
~50% in some patient groups. The sepsis response is triggered by the presence of circulating bacteria which initiate a major Systemic Inflammatory Response Syndrome (SIRS) leading to a range of life threatening consequences.

One of the most acute problems is an elevation of circulating adrenomedullin, a hormone which causes increased permeability of blood vessels and vasodilatation leading to reduced perfusion and organ failure. Adrenomedullin's role in septic shock involves an interaction with a bacterial cell wall protein, stimulating a cytokine storm in the patient. Accordingly, adrenomedullin is an established target for treatment in septic shock/SIRS.

Adrenomedullin has three closely related heteromeric receptors CGRP, AM1 and AM2 and two of these have been subject to drug development studies. Specifically, CGRP inhibition has led to treatments for migraine while our team has developed AM2 inhibitors for the treatment of pancreatic cancer. In this programme, we will target AM1 with a view to preventing adrenomedullin promoted reduction in blood pressure. Currently, a monoclonal antibody (Adrecizumab) to AM1 is starting phase II trials for septic shock after strong preclinical results. In addition, a truncated adrenomedullin peptide is in development
as a competitive hormone antagonist. However, antibodies remain in the system for long periods, and peptides are short-lived. Both are costly to manufacture and hard to store, so a small molecule AM1 receptor antagonist with an appropriately engineered half-life would have considerable advantages.

Small molecule therapeutics continue to receive significant attention from the pharmaceutical R&D community. Improvements in computing power and increasing costs of drug development mean that efforts are now focused on 'intelligent' discovery processes. Accordingly, using an existing AM1 X-ray crystal structure, we will combine modelling and cheminformatics methods to identify novel chemical space with potential to generate biologically-active compounds using Molecular-matched pair analysis. Machine learning will identify key features associated with bioactivity and build these features into molecular design. They will produce a small library of compounds suitable for biological screening that will seed a new drug discovery collaboration (IP owned by Uni of Sheffield). Partnering with Redbrick Molecular, a spinout from Universities of Leeds and Sheffield, means that the novel chemistry will be accessible to the broader pharmaceutical R&D community through catalogue sales of novel chemical building blocks.