Five years ago, Lynn Durham founded a biotech startup — called Stalicla — that aims to do what nobody has achieved so far: tailor pharmacological therapies to idiopathic autism subgroups, or those who have autism with no known genetic cause. To date, the company, which is based in Switzerland, has secured 29 million Swiss francs ($30 million) and developed a machine-learning platform that sorts autistic people into different groups based on shared biological ‘signatures’ and then identifies drugs that it hopes will reverse such signatures.
In March, Stalicla announced that its lead drug candidate, STP1, indicated for a subset of autistic people, is safe and elicited improvements in markers of brain function. Stalicla’s scientific officers also say preliminary results suggest that STP1 eases some behavioral difficulties associated with autism. David Beversdorf, professor of radiology, neurology and psychological sciences at the University of Missouri in Columbia, was so convinced by Stalicla’s approach that he joined its clinical advisory board. “If you can biologically define subtypes that have specific treatment responses, that’s fantastic,” he says.
But the company has not revealed the machinations of its platform or full trial results, which means there is still little for the public to learn. Though identifying autism subtypes based on biological signatures to predict drug responses “seems a reasonable approach,” says Valerie Hu, professor emeritus of biochemistry and molecular medicine at George Washington University in Washington, D.C., for now “we have to wait and see what the results are.”