# A Precision Medicine Tool for Optimal Personalized Treatment in Patients with Acute Myeloid Leukemia

> **NIH NIH R42** · SANICKA, INC · 2022 · $396,908

## Abstract

Acute myeloid leukemia (AML) is an aggressive cancer of the bone marrow and peripheral blood with poor
prognosis mostly due to relapse. Despite decades of improvements in chemo-immunotherapy (CIT) and, more
recently, the use of hypomethylating agent (HMAs) and addition of novel small molecule inhibitors (SMIs) to
back-bone chemotherapy, AML treatment selection and dosage remains mostly empiric, with standard first- and
second-line regimens, each with potential toxic consequences; dosing is based on body surface area, renal and
hepatic function and pharmacokinetics/pharmacodynamics (PK/PD), ignoring tumor-specific parameters (tumor
bulk, heterogeneity and cell cycle kinetics). Consequently, up to 60% of patients are under- or over-dosed and
a further 10-40% of patients have primary refractory disease (non-responders) to gold-standard of care first-line
CIT resulting in poor outcomes with high healthcare costs. Advances in genomic techniques are now able to
assess AML clonal dynamics and measurable residual disease in patients throughout therapy with reasonable
turn-around times. This rapid growth in diagnostic capabilities in conjunction with an ever-increasing number of
available FDA-approved targeted treatments for patients with AML, present a constant and ongoing gap between
practice and potential resulting in significant lag-time between use and know-how to improve outcomes. A
framework for personalized treatment selection and optimization is therefore an unmet need in precision therapy
for patients with AML. To address this need, “πCITTM Simulator”, a Clinical Decision Support service, was
developed to assist Oncologists with treatment selection by providing (before treatment begins) simulations of
disease response, progression, AML clonal evolution and normal blood count recovery in patients receiving
therapy with different CIT, SMI and HMA options and combinations. In order to improve on the selected treatment
for best patient outcome and reduced toxicity, “πCITTM Optimizer”, a Software as Medical Device, was developed
to optimize drug, dose and schedule. πCITTM Simulator and Optimizer provide healthcare professionals with
critical data, prior to treatment initiation, to prevent over- or under-dosage and administration of ineffective drugs
for patients with resistant disease, thereby reducing treatment and hospitalization costs. In Phase 1 of this fast-
track application, SANICKA will develop its first minimum viable product by (1) expanding πCITTM to incorporate
novel SMIs/HMAs resulting in the launch to the market of πCITTM Simulator and (2) creating a web-based
Clinician Portal for Oncologists to upload patient and tumor data, and visualize results. During Phase 2,
SANICKA will (1) expand πCITTM Optimizer to capture AML sub-clonal kinetics and sensitivity to CIT/SMIs/HMAs
using retrospectively-collected multi-center patient data for validation and (2) prospectively validate πCITTM
Optimizer with an observational study in patients with AM...

## Key facts

- **NIH application ID:** 10547266
- **Project number:** 1R42CA272132-01A1
- **Recipient organization:** SANICKA, INC
- **Principal Investigator:** Athanasios Mantalaris
- **Activity code:** R42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $396,908
- **Award type:** 1
- **Project period:** 2022-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10547266

## Citation

> US National Institutes of Health, RePORTER application 10547266, A Precision Medicine Tool for Optimal Personalized Treatment in Patients with Acute Myeloid Leukemia (1R42CA272132-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10547266. Licensed CC0.

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