Wednesday, December 23, 2020
Laura Perez-Cano is Head of Discovery and Deputy Head of Computational Systems Biology at STALICLA. Prior to joining STALICLA, she was a postdoctoral researcher at David Geffen School of Medicine at University of California Los Angeles (UCLA) – focusing on genetic involvement in Autism Spectrum Disorder (ASD). Laura’s move to STALICLA was driven by a desire to apply her academic experience to a translational initiative, and her belief in the potential of precision medicine to benefit people living with neurodevelopmental disorders (NDDs).
What do we know about the genetic origins of ASD?
In the last decade, we have made remarkable progress in gene discovery to identify abnormal biological and molecular pathways in NDDs, including ASD. This research has revealed several risk genes and mutations involved in ASD. However, statistical modelling suggests that there are more than 1000 genes involved in NDDs that are yet to be discovered. The number of genes concerned and the multitude of disrupted biological pathways explain why ASD is such a highly diverse and complex disorder.
How will this research help to develop a precision medicine for the condition?
Large-scale genetic studies and genome sequencing have revealed the complexity and heterogeneity within ASD – the challenge now is to stratify the condition into more homogeneous subgroups of patients. Streamlining patients according to patterns within groups of ASD subtypes allows us to select and develop personalized and efficient drug candidates capable of correcting specific biological disruptions implicated in the pathophysiology of the condition.
STALICA developed the DEPI platform to help to group ASD patients into subtypes. What is DEPI and how does it work?
DEPI (Databased Endophenotyping Patient Identification) is an artificial intelligence driven platform that applies systems biology and multi-omics to bring precision medicine to patients with NDDs. While there are similar initiatives in other fields, such as oncology, DEPI is a first-in-class in the field of NDDs, allowing the identification of non-behavioral biomarkers to define homogeneous and clinically actionable subgroups of patients. DEPI’s capabilities allow two possibilities in developing precision medicine – one is matching a defined subgroup of patients to a drug candidate that can target those in a precise and efficient way, and the second is identifying drug candidates that can correct specific biological disruptions and matching these to specific patient subgroups that are likely going to respond positively to the treatment with such drug candidates.
DEPI has allowed us to define a patient subgroup (ASD-Phen1) that could account for up to twenty-five percent of ASD patients who share similar biological perturbations. Our recently announced Phase 1b clinical trial is investigating a new therapeutic combination treatment (STP1) in this subgroup.
STALICLA has its own pipeline of drug candidates, which include STP1, and we are also exploring collaborative projects to develop promising drug candidates with third party strategics.
What are the technologies that have enabled this research to advance?
Advancements in the development of 'omics' technologies, which enable a multi-scale detection of genes, proteins and metabolites in a specific biological sample, as well as methodological advancements in the field of computational systems biology, have all contributed to STALICLA’s research. These technologies have given us valuable insights into a patient’s cellular make-up and helped to really shift our understanding of complex diseases.
Do you have a vision of how a platform like DEPI could be used in diagnosis and treatment in a real-life setting?
Today, DEPI is focused on developing precision medicine-based treatment options for patients diagnosed with ASD and other NDDs. Our goal is that one day we will also be able to apply this sophisticated artificial intelligence platform, with its increasing number of anonymized patient profiles and immense volume of biological and clinical data, as a scientifically proven methodology for diagnosing ASD subtypes. Currently, diagnosis is based upon behavior, so a definitive test that identifies the form of ASD could be a game changer for more personalized treatment protocols. Using DEPI as a screening tool would enable doctors to identify specific therapeutic treatments to address the core symptoms of a patient’s particular pathology. That level of informed decision-making and precision medicine would truly improve patients’ lives.