Open Targets Platform 22.11 has been released!

Open Targets Platform Nov 24, 2022

The latest release of the Platform — 22.11 — is now available at platform.opentargets.org.

Key points

In addition to continuous updates from our data providers, we have introduced the following new features:

Gene burden data for Parkinson’s disease
Updated classifications for clinical trial stop reasons
Variant functional consequences, available to browse in the Gene2Phenotype and Orphanet widgets

Key stats

Metric	Count
Targets	62,678
Diseases	22,274
Drugs	12,854
Evidence	14,611,717
Associations	6,960,486

Additional metrics are available on the Open Targets Community.

New gene burden data

In the largest genome-wide analysis of rare variation in Parkinson’s disease to date, Makarious et al. (preprint) identified several potentially novel associations. Using whole genome and whole exome sequencing data for 7,184 individuals with Parkinson’s disease, 6,701 proxy cases, and 51,650 neurologically healthy controls, they performed a meta-analysis of protein-altering variants with moderate to large effect sizes. The study features multiple cohorts of European ancestry.

This collaborative work from the Accelerating Medicines Partnership Parkinson’s Disease (AMP-PD) initiative, the National Institutes of Health, the UK Biobank, and Genentech, has now been integrated into the Open Targets Platform’s Gene Burden widget. This work provides evidence of association for 9 genes, including 7 for which this is the first evidence linking them to Parkinson’s disease in the Platform (B3GNT3, AUNIP, ADH5, TUBA1B, OR1G1, CAPN10, and TREML1).

Gene Burden widget in the Open Targets Platform for the association of B3GNT3 with Parkinson’s disease. This is the first evidence for this association in the Platform, and it also provides new evidence for the role of neuroinflammation in Parkinson’s disease.

For most of the genes, the signal could be replicated in multiple meta-analyses. For the 3 genes which didn’t show exome-wide significance in the meta-analyses (OR1G1, TUBA1B, and ADH5), we report the test and cohort in which statistical significance was reached.

Updated classifications for clinical trial stop reasons

We have updated the classifier algorithm we use to categorise the reasons for which clinical trials stopped before their endpoints were met, improving the overall accuracy by 20%.

This feature was introduced in our 22.04 release, and allows users to see the full text reason recorded on ClinicalTrials.gov, as well as the category or categories assigned by our model when browsing evidence in the ChEMBL widget.

ChEMBL widget showing clinical trials and/or approved drugs targeting JAK2 and indicated for ulcerative colitis. Hovering over the tooltip when a clinical trial’s status is Terminated shows the recorded trial stop reason, as well as the category or categories assigned by our classifier. Here, the stop reason for a terminated Phase II trial for Izencitinib is classified as ‘Business or Administrative’ and ‘Negative’.

The updated model is trained on the basis that multiple classes can be assigned for the same recorded reason, allowing the classifier to learn patterns of correlation between the 17 categories. This means that the model learns to predict categories in the context of the others.

Some of the evidence we integrate from Gene2Phenotype and Orphanet includes information on the functional consequences of variants, which is available to browse in the API. We have added a new column to the widgets to include this information in the web application. Please note, it can be null.

Orphanet and Gene2Phenotype widgets showing genetic evidence linking RAF1 and Noonan Syndrome. Gene2Phenotype reports two variants in which the functional consequence is altered gene product structure, while Orphanet report one gain of function variant.

Variants that change the sequence of the translated protein can have functional consequences, for example: altering the protein structure, removing or adding post-translational modification sites, and altering ligand or co-factor binding sites. This is key information to understand how the variant may be involved in the disease mechanism.