Researchers from the University of Cambridge have designed and used an artificial-intelligence (AI)-based approach to advance drug design and accelerate the search for Parkinson’s disease (PD) treatments.

Researchers used AI to identify compounds that block the clumping or aggregation of alpha-synuclein, the key protein that characterises PD.

Affecting more than six million people worldwide, PD is a progressive neurological condition that leads to a slow deterioration of parts of the brain.

As well as motor symptoms, PD can also affect the gastrointestinal system, nervous system, sleeping patterns, mood and cognition and can contribute to a reduced quality of life and significant disability.

Researchers developed and used a machine learning technique to screen a chemical library that contained millions of entries to identify small molecules that bind to the amyloid aggregates and block their proliferation.

The process of screening for drug candidates among large chemical libraries can be time-consuming, expensive and often unsuccessful.

The research team successfully sped up the initial screening process ten-fold and identified five highly potent compounds to be further investigated while reducing the cost by a thousand-fold, meaning that potential treatments for PD could reach patients much faster.

“Using the knowledge gained from the initial screening, we were able to train the model to identify the specific regions on these small molecules responsible for binding to re-screen and find more potent molecules,” explained Michele Vendruscolo, co-director of the Centre for Misfolding Diseases, University of Cambridge.

In doing so, researchers developed compounds to target pockets on the surfaces of the aggregates responsible for the exponential proliferation of the aggregates themselves, which were hundreds of times more potent and cheaper to develop.

A phase 3 international trial evaluating a new personalised immunotherapy treatment for melanoma has recruited its first patients at University College London Hospitals (UCLH) NHS Foundation Trust.

Results from the phase 2 study evaluating Moderna and Merck & Co’s – known as MSD outside the US and Canada – mRNA-based technology treatment were published in The Lancet.

Currently the fifth most common type of cancer, melanoma is a serious form of skin cancer that is responsible for over 8,000 new cases every year.

The condition is characterised by the uncontrolled growth of pigment-producing cells.

The new trial, INTerpath-001, is evaluating mRNA-4157 (V940) in combination with Merck’s PD-1 inhibitor, Keytruda (pembrolizumab), versus Keytruda on its own.

It is a risk-reducing treatment option for patients with resected, high-risk, stage 2b to 4 melanomas following surgical removal of the cancer, with primary outcomes of recurrence-free survival and overall survival and metastasis-free survival as secondary outcomes.

The personalised treatment is designed to instruct the body to make up to 34 proteins that target neoantigens – proteins found only on cancer cells – identified by gene sequencing that are thought to be driving the cancer in that particular patient.

The neoantigen therapy works to prime the immune system to attack the tumour cells in each patient, while Keytruda works to block an immunological ‘brake’ that protects the cancer.

Heather Shaw, UCLH consultant medical oncologist, commented: “The immunotherapy can prepare the immune system to quickly identify and attack any cancer cells bearing them, with the aim of preventing the recurrence of melanoma.”
Researchers hope that the study running across several sites in the UK will confirm findings from the earlier trial and enrol over 1,000 patients worldwide.