Molecular Health Gmbh

University of Texas System

David B Jackson

Anil K Sood

Stephan Brock

Alexander Zien

Guillaume Taglang

Theodoros Soldatos

Martin Stein

Hartmut Voss

Alexander Picker

Bernhard Sulzer

Christopher G Danes

The present disclosure relates to systems and methods for bioinformatics and data processing. In particular, in a first aspect, the present disclosure relates to methods and systems for generating a personalized treatment guideline for a patient and for selecting a treatment for a patient. In another aspect, the present disclosure relates to methods and systems for selecting patients for a clinical trial of a treatment. The invention resolves cases in which patients have more than one 'actionable' aberration by combining the patient-specific molecular information and the treatment-specific molecular information further with a clinico-molecular disease model, specifically a scoring of genes and/or proteins that represents several aspects of their involvement into the disease. In this way, treatments and patients can be prioritized that are most likely to impact or impacted by the disease mechanism, respectively.

Systems and methods for clinical decision support

The present disclosure describes systems and methods for using patient-specific genomic information to optimize or de-risk therapy for the patient. A user may identify a medication for consideration for prescription to a patient, and a genetic variant of the patient affecting a first protein. An analyzer may identify a second medication targeting the first protein, and may retrieve adverse event data from an adverse event database for patients co-medicated with both the first medication and second medication. The analyzer may determine, based on rates of adverse events, the likelihood of an adverse event occurring through co-medication of the first medication and second medication. Based on the likelihood, and based on a correspondence or non-correspondence between a protein activation characteristic of the first medication and the effect of the genetic variant of the patient, the analyzer may indicate or contra-indicate the first medication for the patient.

Systems and methods for personalized de-risking based on patient genome data

The present disclosure is directed to systems and methods for identifying unknown drug targets via adverse event data. An analyzer receives an identification of a first drug having one or more unknown target proteins and identifies a second drug related to the first drug. The analyzer retrieves, from an adverse event database, a first side effect profile associated with the first drug, and a second side effect profile associated with the second drug. The analyzer generates a third side effect profile comprising a subset of the first side effect profile not shared by the second side effect profile, and identifies a third drug having a fourth side effect profile comprising the third side effect profile. The analyzer retrieves a list of one or more target proteins of the third drug not targeted by the second drug, and presents the retrieved list as potential target proteins of the first drug.

Systems and methods for identifying unknown drug targets via adverse event data

The present disclosure describes systems and methods for predicting a likely side effect profile for even new, untested medications. A predicted side effect profile may be generated based on intersections of side effect profiles of other medications that affect the same or related molecular entities, such as the nearby target proteins, involve the same pathways, or are otherwise similarly related. To generate a predicted side effect profile for a new drug targeting a novel or previously un-targeted protein target, an analyzer may query an adverse event database for records pertaining to patients who have taken drugs or combinations of drugs that target or affect molecular entities in the vicinity of the novel target within a global molecular entity graph, and, in some embodiments, may retrieve a plurality of adverse event records and generate an intersection of side effects associated with related targets to predict likely side effects for the novel target.

Systems and methods for using adverse event data to predict potential side effects

The present disclosure is directed to systems and methods for identifying unknown drug targets via adverse event data. An analyzer receives an identification of a first drug having one or more unknown target proteins and identifies a second drug related to the first drug. The analyzer retrieves, from an adverse event database, a first side effect profile associated with the first drug, and a second side effect profile associated with the second drug. The analyzer generates a third side effect profile including a subset of the first side effect profile not shared by the second side effect profile, and identifies a third drug having a fourth side effect profile including the third side effect profile. The analyzer retrieves a list of one or more target proteins of the third drug not targeted by the second drug, and presents the retrieved list as potential target proteins of the first drug.

There is disclosed a molecular composition(s) of a novel tissue protective erythropoietin (EPO) binding receptor protein complex, termed NEPOR. Presence of NEPOR components on a tumor allows EPO to impinge on the survival of associated cells thereby enhancing tumor progression and negatively effecting patient survival. Presence of NEPOR represents a prognostic biomarker for poorer patient outcome. Thus, methods are provided for stratifying patients having a tumor as suitable (i.e. NEPOR not present) or non-suitable (i.e., NEPOR present) for EPO treatment, comprising: (a) isolating a tissue sample from an individual who is receiving or is a candidate for receiving erythropoietin, (b) determining the level of expression of the NEPOR gene(s) (mRNA) and/or the presence of the NEPOR gene product (protein) from the isolated tissue, and (c) correlating the presence of an NEPOR gene expression product or the presence of NEPOR protein to a physiological response to the treatment with erythropoietin. Furthermore, by disclosing the molecular compositions of NEPOR species, there are disclosed methods for rationally identifying/designing NEPOR modulating therapeutics. Methods also are provided for treating neurological insults such as stroke (via enhancement of NEPOR activity) and cancer (via down-regulation of cyto-protective signaling from NEPOR).

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Heidelberg, Germany

David B Jackson