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Personalization, Prediction & Precision

SANICKA is developing clinical decision support tool for the selection and optimization of treatment for patients with acute myeloid leukemia (AML). πCITTM overcomes existing barriers for best use of chemo-immunotherapy (CIT) by assisting hematologist-oncologists with patient-specific selection of standard-of-care and novel leukemia treatments including the optimal drug, along with its dose and schedule for the duration of treatment a priori.

About Acute Myelogenous Leukemia (AML)

AML is a cancer of the bone marrow and the blood. It progresses rapidly without treatment and affects mostly cells that aren’t fully developed; therefore, these cells can’t carry out their normal functions. AML can be a difficult disease to treat, and researchers are studying new approaches to AML therapy in clinical trials. Learn more

AML in Adults: AML is also called acute myelocytic leukemia, acute myelogenous leukemia, acute granulocytic leukemia or acute non-lymphocytic leukemia. It is most common in older people Learn more. For information on AML in children, see Leukemia in Children. Learn more


The core of the technology is a mechanistic/deterministic algorithm that uses patient- and tumor-specific data to simulate the response to different treatment alternatives, providing dynamic predictions of the tumor response and patient recovery prior, during and after therapy.

Patient Workflow
Selected Publications
PK / PD Models

Chemotherapy drug scheduling for the induction treatment of patients with acute myeloid leukemia.

IEEE Transactions on Biomedical Engineering

AML & Immune Response

Quantification and cytokine production of circulating lymphoid and myeloid cells in acute myelogenous leukemia (AML).

Leukemia 2003

AML & Immune Response

Immune reconstitution and clinical recovery following anti-CD28 antibody (TGN1412)-induced cytokine storm.

Cancer Immunol, Immunotherapy 2021

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