Fragile X syndrome (FXS) is the most frequent monogenic cause of autism or intellectual disability, and research on its pathogenetic mechanisms has provided important insights on this neurodevelopmental condition. Nevertheless, after 30 years of intense research, efforts to develop treatments have been mostly unsuccessful. The aim of this review is to compile evidence from existing research pointing to clinical, genetic, and therapeutic response heterogeneity in FXS and highlight the need of implementing precision medicine-based treatments. We comment on the high genetic and phenotypic heterogeneity present in FXS, as a contributing factor to the difficulties found during drug development. Given that several clinical trials have showed a non-negligeable fraction of positive responders to drugs targeting core FXS symptoms, we propose that success of clinical trials can be achieved by tackling the underlying heterogeneity in FXS by accurately stratifying patients into drug-responder subpopulations. These precision medicine-based approaches, which can be first applied to well-defined monogenic diseases such as FXS, can also serve to define drug responder profiles based on specific biomarkers or phenotypic features that can associate patients with different genetic backgrounds to a same candidate drug, thus repositioning a same drug for a larger number of patients with NDDs.

Copy-number variants (CNVs) are genome-wide structural variations involving the duplication or deletion of large nucleotide sequences. While these types of variations can be commonly found in humans, large and rare CNVs are known to contribute to the development of various neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD). Nevertheless, given that these NDD-risk CNVs cover broad regions of the genome, it is particularly challenging to pinpoint the critical gene(s) responsible for the manifestation of the phenotype. In this study, we performed a meta-analysis of CNV data from 11,614 affected individuals with NDDs and 4,031 control individuals from SFARI database to identify 41 NDD-risk CNV loci, including 24 novel regions. We also found evidence for dosage-sensitive genes within these regions being significantly enriched for known NDD-risk genes and pathways. In addition, a significant proportion of these genes was found to (1) converge in protein-protein interaction networks, (2) be among most expressed genes in the brain across all developmental stages, and (3) be hit by deletions that are significantly over-transmitted to individuals with ASD within multiplex ASD families from the iHART cohort. Finally, we conducted a burden analysis using 4,281 NDD cases from Decipher and iHART cohorts, and 2,504 neurotypical control individuals from 1000 Genomes and iHART, which resulted in the validation of the association of 162 dosage-sensitive genes driving risk for NDDs, including 22 novel NDD-risk genes. Importantly, most NDD-risk CNV loci entail multiple NDD-risk genes in agreement with a polygenic model associated with the majority of NDD cases.

Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders (NDDs) with a high unmet medical need. The diagnosis of ASD is currently based on behavior criteria, which overlooks the diversity of genetic, neurophysiological, and clinical manifestations. Failure to acknowledge such heterogeneity has hindered the development of efficient drug treatments for ASD and other NDDs. DEPI^® (Databased Endophenotyping Patient Identification) is a systems biology, multi-omics, and machine learning-driven platform enabling the identification of subgroups of patients with NDDs and the development of patient-tailored treatments. In this study, we provide evidence for the validation of a first clinically and biologically defined subgroup of patients with ASD identified by DEPI, ASD Phenotype 1 (ASD-Phen1). Among 313 screened patients with idiopathic ASD, the prevalence of ASD-Phen1 was observed to be ~24% in 84 patients who qualified to be enrolled in the study. Metabolic and transcriptomic alterations differentiating patients with ASD-Phen1 were consistent with an over-activation of NF-κB and NRF2 transcription factors, as predicted by DEPI. Finally, the suitability of STP1 combination treatment to revert such observed molecular alterations in patients with ASD-Phen1 was determined. Overall, our results support the development of precision medicine-based treatments for patients diagnosed with ASD.

This study aimed to evaluate the safety and tolerability of STP1, a combination of ibudilast and bumetanide, tailored for the treatment of a clinically and biologically defined subgroup of patients with Autism Spectrum Disorder (ASD), namely ASD Phenotype 1 (ASD-Phen1). We conducted a randomized, double-blind, placebo-controlled, parallel-group phase 1b study with two 14-day treatment phases (registered at clinicaltrials.gov as NCT04644003). Nine ASD-Phen1 patients were administered STP1, while three received a placebo. We assessed safety and tolerability, along with electrophysiological markers, such as EEG, Auditory Habituation, and Auditory Chirp Synchronization, to better understand STP1’s mechanism of action. Additionally, we used several clinical scales to measure treatment outcomes. The results showed that STP1 was well-tolerated, with electrophysiological markers indicating a significant and dose-related reduction of gamma power in the whole brain and in brain areas associated with executive function and memory. Treatment with STP1 also increased alpha 2 power in frontal and occipital regions and improved habituation and neural synchronization to auditory chirps. Although numerical improvements were observed in several clinical scales, they did not reach statistical significance. Overall, this study suggests that STP1 is well-tolerated in ASD-Phen1 patients and shows indirect target engagement in ASD brain regions of interest.

Neurodevelopmental disorders (NDDs) are a group of highly heterogenous and prevalent disorders characterized by abnormal brain development. These disorders remain an area of high unmet medical need due to the lack of specific pharmacological treatments addressing the core symptoms, especially social communication deficits. Most clinical trials in NDDs over the past decades have failed to show efficacy across patients recruited using behavior-based diagnosis despite bearing unrelated molecular pathophysiology. Here, we present the DEPI platform, STALICLA’s data-driven solution to meet the biological stratification needs in complex NDD populations. DEPI is the first systems biology and multi-omics based, AI module driven platform in the NDD space for the development of precision medicine-based treatments. It uses curated NDD-risk catalogs to identify pathway-level perturbations associated to clinical observations. Furthermore, it combines supervised and unsupervised machine learning methods to characterize molecular signatures (genetic, transcriptomic and metabolomic) across individuals through mechanistic endophenotyping. Importantly, the platform facilitates matching of the right treatments to the right patients with NDDs. The platform has been proven clinically successful by: i) identifying a first clinically actionable subgroup of patients with Autism Spectrum Disorder (ASD Phenotype 1), and the corresponding tailored treatment, STP1, with outstanding and unprecedented target engagement results in a Phase 1b clinical trial; and ii) blindly recalling known drug responder patients with sensitivity and specificity values ~80% for patients with fragile X syndrome.

Brain morphological abnormalities are common in patients with neurodevelopmental disorders (NDDs) and other neuropsychiatric disorders, often reflecting abnormal brain development and function. Genetic studies have found common genetic factors in NDDs and other neuropsychiatric disorders, although the etiology of brain structural changes in these disorders remains poorly understood. In this study, we analyzed magnetic resonance imaging (MRI) and genetic data from more than 30K individuals from the UK Biobank to evaluate whether NDD-risk copy number variants (CNVs) are also associated with neuroanatomical changes in both patients and neurotypical individuals. We found that the size differences in brain regions such as amygdala, caudate, ventricle, grey matter and cerebellum, among others, were associated with deletions or duplications of specific areas of the human genome, and that these neuroanatomical changes confer risk for neuropsychiatric disorders. Furthermore, we observed that gene sets located in these genomic regions were enriched for pathways crucial for brain development and for phenotypes commonly observed in patients with NDDs. These findings highlight the link between CNVs, brain structure abnormalities and the shared pathophysiology of NDDs and other neuropsychiatric disorders, providing new insights into the underlying mechanisms of these disorders and the identification of potential biomarkers for better diagnosis.