Notable Labs Presents Clinical Platform Validation Data at the 2023 AACR Conference

Notable Labs Presents Clinical Platform Validation Data at the 2023 American Association for
Cancer Research Annual Meeting

– 100% accuracy predicting clinical responders with Notable’s PPMP with enhanced
machine learning –
– Fourth clinical validation study of Predictive Precision Medicine Platform

FOSTER CITY, Calif., April 18, 2023 – Notable Labs, Inc. (“Notable”), a clinical stage therapeutic
platform company developing predictive precision medicines for cancer patients, today
announced clinical data regarding its Predictive Precision Medicine Platform (PPMP) at the 2023
American Association for Cancer Research (AACR) Annual Meeting being held in Orlando,
Florida from April 14-19, 2023.
“Together with our collaborators at Washington University, today we are reporting on the
fourth successful validation study of our Predictive Precision Medicine Platform (PPMP),” said
Thomas Bock, M.D., Chief Executive Officer of Notable. “The study assessed our platform’s
accuracy in predicting whether a patient will clinically respond to their induction chemotherapy
for acute myeloid leukemia (AML). We are excited about these clinical results as they not only
corroborate, but expand upon, those of our three other validation trials. Using a specially
designed approach to training our machine learning algorithm, PPMP achieved 100% accuracy
in its predictions for response to venetoclax plus decitabine (VenDec). That is, all patients
predicted to respond clinically actually did while those patients predicted not to respond, did
not. These results add further validation and promise to Notable’s strategy of de-risking
precision medicines and developing them selectively in patients predicted to clinically

Abstract title: Predictive precision medicine platform accurately predicts individual patient
response to AML treatments to maximize outcomes.

This study assessed the capacity of Notable’s Predictive Precision Medicine Platform to
accurately predict newly diagnosed AML patients’ response to treatment with cytarabine plus
idarubicin (7+3) or VenDec. Employing two different training methods, the predictive algorithm
assessed pre-induction blood samples from 31 patients, 18 of whom received 7+3 and 13 of
whom received VenDec. The “original” training approach bases predictions on the number of
live blasts remaining after treatment with the induction therapies, while the enhanced PPMP
approach employs a novel machine learning method and is explicitly designed to maximize the
accuracy of predictions for VenDec. The study assessed the correlations between predictive and
actual outcomes using four metrics: positive predictive value (PPV/predictive precision, the
proportion of predicted responders who actually responded), the negative predictive value
(NPV, the proportion of predicted non-responders who, in fact, did not respond), the area
under the curve (AUC) of the Receiver Operating Characteristic (ROC) curve (the probability that
the predictor correctly ranks a randomly chosen responder higher than randomly chosen nonresponder),
and accuracy (the proportion of correct predictions out of all predictions).

For both 7+3 and VenDec, PPMP trained using the original method achieved a PPV of 100%, i.e.,
all predicted responders actually responded. A PPV of 100% indicates, for example, that a
clinical trial selectively enrolling predicted clinical responders would result in a 100% response
rate. The NPV was 67% for 7+3 and 57% for VenDec (i.e., 67% of patients on 7+3 and 57% on
VenDec who were predicted not to respond, did not ). Trained using the original method AUC
was 0.91 for 7+3 and 0.81 for VenDec, and the accuracy of this approach was 94% for 7+3 and
77% for VenDec.
To further increase the accuracy on VenDec, a machine learning algorithm integrated the
behavior of malignant and non-malignant cell populations and examined responses to the
therapeutics along multiple biological dimensions. This novel enhanced method resulted in
100% PPV, 100% NPV and 100% accuracy on VenDec.
These compelling results highlight the platform’s potential as a tool for guiding the
identification of, the decision-making regarding, and the clinical development of optimal AML
therapies for the individual patient.
Additional meeting information can be found on the AACR website, The
poster will be available on the Company’s website at shortly after the

Presentation Poster (Click to Download PDF)

About Notable Labs, Inc.
Notable Labs, Inc. is a clinical-stage platform therapeutics company developing predictive
precision medicines for patients with cancer. Through its proprietary Predictive Precision
Medicines Platform (“PPMP”), Notable bio- simulates a cancer treatment and predicts whether
or not a patient is likely to respond to that specific therapeutic. Notable’s PPMP can identify
and select clinically responsive patients prior to their treatment and thus fast-track clinical
development in this patient population. By continually advancing and expanding the reach of
the PPMP across diseases and predicted medical outcomes, Notable aims to be the leader in
precision medicine and revolutionize the way in which patients seek and receive treatments
that work best for them – patient by patient and cancer by cancer. Notable believes it has
created a targeted and de-risked in-licensing strategy to deliver a product’s medical impact and
commercial value faster, higher, and with a greater likelihood of success than traditional drug
development. By transforming historical standards of care, Notable aims to create dramatic
positive impact for patients and the healthcare community. In February 2023, Notable entered
into a definitive agreement to merge with VBL Therapeutics (NasdaqCM: VBLT). Notable is
headquartered in Foster City, California. Learn more at and follow us

Investor Relations: Daniel Ferry, LifeSci Advisors
+1 (617) 430-7576,