Karydo TherapeutiX, Inc.

Human diseases are characterized by multiple features such as their pathophysiological, molecular and genetic changes. The rapid expansion of such multi-modal disease-omics space provides an opportunity to re-classify diverse human diseases and to uncover their latent molecular similarities, which could be exploited to repurpose a therapeutic-target for one disease to another.

Herein, we probe this underexplored space by soft-clustering 6955 human diseases by multi-modal generative topic modeling. Focusing on chronic kidney disease and myocardial infarction, two most life-threatening diseases, unveiled are their previously underrecognized molecular similarities to neoplasia and mental/neurological-disorders, and 69 repurposable therapeutic-targets for these diseases. Using an edit-distance-based pathway-classifier, we also find molecular pathways by which these targets could elicit their clinical effects. Importantly, for the 17 targets, the evidence for their therapeutic usefulness is retrospectively found in the pre-clinical and clinical space, illustrating the effectiveness of the method, and suggesting its broader applications across diverse human diseases.

The code reported in this article is available at: https://github.com/skozawa170301ktx/MultiModalDiseaseModeling.

Supplementary data are available at Bioinformatics Advances online.

Approximately 90% of pre-clinically validated drugs fail in clinical trials owing to unanticipated clinical outcomes, costing over several hundred million US dollars per drug. Despite such critical importance, translating pre-clinical data to clinical outcomes remain a major challenge. Herein, we designed a modality-independent and unbiased approach to predict clinical outcomes of drugs. The approach exploits their multi-organ transcriptome patterns induced in mice and a unique mouse-transcriptome database "humanized" by machine learning algorithms and human clinical outcome datasets. The cross-validation with small-molecule, antibody, and peptide drugs shows effective and efficient identification of the previously known outcomes of 5,519 adverse events and 11,312 therapeutic indications. In addition, the approach is adaptable to deducing potential molecular mechanisms underlying these outcomes. Furthermore, the approach identifies previously unsuspected repositioning targets. These results, together with the fact that it requires no prior structural or mechanistic information of drugs, illustrate its versatile applications to drug development process.

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive disease characterized by complex lung pathogenesis affecting approx. three million people worldwide.While the mol. and cellular details of the IPF mechanism is emerging, our current understanding is centered around the lung itself.On the other hand, many human diseases are the products of complex multi-organ interactions.Hence, we postulate that a dysfunctional crosstalk of the lung with other organs plays a causative role in the onset, progression and/or complications of IPF.In this study, we employed a generative computational approach to identify such inter-organ mechanism of IPF.This approach found unexpected mol. relatedness of IPF to neoplasm, diabetes, Alzheimers disease, obesity, atherosclerosis, and arteriosclerosis.Furthermore, as a potential mechanism underlying this relatedness, we uncovered a putative mol. crosstalk system across the lung and the liver.In this inter-organ system, a secreted protein, kininogen 1, from hepatocytes in the liver interacts with its receptor, bradykinin receptor B1 in the lung.This ligand-receptor interaction across the liver and the lung leads to the activation of calmodulin pathways in the lung, leading to the activation of interleukin 6 and phosphoenolpyruvate carboxykinase 1 pathway across these organs.Importantly, we retrospectively identified several pre-clin. and clin. evidence supporting this inter-organ mechanism of IPF.In conclusion, such feedforward and feedback loop system across the lung and the liver provides a unique opportunity for the development of the treatment and/or diagnosis of IPF.Furthermore, the result illustrates a generative computational framework for machine-mediated synthesis of mechanisms that facilitates and complements the traditional exptl. approaches in biomedical sciences.