The Science

Predictive Precision Proteomics is the future

The underlying basis of cancer is genomic alterations which inevitably result in dysregulated protein activity that drives the disease. Accordingly, while a powerful tool to uncover underlying mechanisms of disease, the utility of genomics for patient selection is limited when it comes to drug-response prediction in oncology. Genomic biomarkers have only proven useful for patient selection in the very small subset of cancers caused by single gene alterations or simple synthetic lethal context. However, the vast majority of cancers contain multiple, complex genomic alterations resulting in the dysregulated tumor-driving protein activity.

Proteomic biomarkers enable direct measurement of disease-driving mechanisms independent of target gene alterations, and allows for accurate matching with the  mechanism of action of a particular drug.

Acrivon’s Predictive Precision Proteomics (AP3) platform takes proteomics one step further. Our transformative method is widely applicable allowing us to deliver impactful therapies to precisely the right patients.

Acrivon’s Predictive Precision Proteomics – A paradigm change in precision drug development

AP3 is a proprietary, streamlined approach to develop patient selection tumor biopsy tests, called OncoSignature tests, designed to identify the patients most likely to benefit from any particular drug. The method is broadly applicable and we specifically apply it to our pipeline of clinically active drug candidates as well as carefully selected preclinical lead series with a strong clinical rationale  for which there is no obvious patient selection path through standard companion diagnostic approaches. AP3 is a transformative, efficient method to accurately match the right therapy to the right patient.

01

Biomarker Identification

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Biomarker Identification

Our method is engineered to be agnostic to the underlying genetic alterations and enables identification and selection of patients based on direct protein measurement of the disease-driving mechanisms that are regulated by and sensitive to the drug. Our AP3 platform is based on two underlying technology pillars typically executed in two sequential steps: the first step, high-resolution mass spectrometry for biomarker identification which is integrated with the second step, our automated tumor biopsy-imaging platform that enables biomarker validation and which is also used to run our OncoSignature tests.

02

 Test

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Non-responder Responder
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One of the key outputs of our biomarker identification workflow is translated into clinically actionable, drug-specific and proprietary OncoSignature tests. These are drug-tailored, automated, quantitative proteomic tissue imaging assays validated on and applied to pretreatment tumor biopsies for patient selection as a companion diagnostic (CDx) in clinical trials.

03

Patient Responders

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Patient Responders

We are developing drug candidates to address prevalent, high unmet need cancer types with multiple driver mutations where patient responder identification has proven challenging, and where we believe there can be a significant impact for patients. Our proteomics approach uncovers drug sensitivity and allows for accurate patient responder selection, as well as exclusion of those predicted not to benefit from the treatment.

Our science delivers

The AP3 method efficiently produces a number of valuable, clinically actionable deliverables. These will positively impact the patients receiving our drug candidates as well as future drug development.

AP3
Patient responder identification
Indication finding
Rational drug combinations
Resistance mechanisms
Pharmacodynamic (PD) biomarkers

We enable patient response prediction

By identifying patients who are most likely to respond to treatment, our goal is to only treat patients most likely to benefit from the drug and avoid overtreatment of patients that do not benefit from it with the potential for side effects.

We identify drug-sensitive cancer types not previously treated

Using AP3, we can uncover additional cancer indications and tumor types where the drug candidate is predicted to be effective.

We reveal rational drug combinations

By targeting the identified drug resistance mechanisms we can design rational combination strategies, as well as fine-tune drug dose and scheduling.

We uncover drug resistance mechanisms

Using AP3 drug profiling we learn about specific biomarkers and pathways related to resistance mechanisms.

We identify pharmacodynamic (PD) biomarkers

These markers help us understand biological effects of our drug candidates and inform our decision making, such as establishing doses and dosing regimens to improve future clinical studies.

Publications & Posters

Rational design of ACR-2316, a novel, potent WEE1/PKMYT1 inhibitor with superior single agent activity using Acrivon Predictive Precision Proteomics (AP3) (EORTC-NCI-AACR, 2024)

ACR-2316: a potent, selective WEE1/PKMYT1 inhibitor rationally designed for superior single agent activity using Acrivon Predictive Precision Proteomics (AP3) for biological SAR (HUPO, 2024)

Acrivon Predictive Precision Proteomics (AP3)-guided development and prospective clinical registrational-intent Phase 2 validation of the response-predictive OncoSignature test for the CHK1/2 inhibitor, ACR-368 (HUPO, 2024)

A Phase 2 study of ACR-368 in patients with endometrial carcinoma and prospective validation of OncoSignature patient selection (NCT05548296), (ESMO, 2024)

ACR-2316: A Potentially First-in-Class, Potent, Selective WEE1/PKMYT1 Inhibitor Rationally Designed for Superior Single Agent Activity Through Synergistic Disruption of Cell Cycle Checkpoints (AACR, 2024)

Acrivon Predictive Precision Proteomics (AP3) Uncovers Mechanism of Resistance to ACR-368, a Clinical-Stage CHK1/2 Inhibitor, and Identifies Rational Combination Treatment (AACR, 2024)

Identification of Biomarkers Predictive of Sensitivity to the CHK1/2 Inhibitor ACR-368 Using High-Resolution Phosphoproteomics and Development of an ACR-368-Tailored Patient Responder Identification 3-Marker Test, ACR-368 OncoSignature. (AACR-NCI-EORTC Congress, 2023)

Validation of the OncoSignature Assay, an ACR-368-Tailored Response-Predictive Quantitative Multiplexed Immunofluorescent Assay for Prediction of Sensitivity to the CHK1/2 Inhibitor ACR-368 in Individual Patients with Cancer. (AACR-NCI-EORTC Congress, 2023)

A Phase 2 study of prexasertib (LY2606368) in platinum resistant or refractory recurrent ovarian cancer (Gynecologic Oncology, 2022)

Phosphoproteomics of primary AML patient samples reveals rationale for AKT combination therapy and p53 context to overcome selinexor resistance (Cell Reports, 2022)

Proteomics of Resistance to Notch1 Inhibition in Acute Lymphoblastic Leukemia Reveals Targetable Kinase Signatures. (Nature Communications, 2021)

Prexasertib: an Investigational Checkpoint Kinase Inhibitor for Treatment of High-Grade Serous Ovarian Cancer. (Expert Opinion on Investigational Drugs, 2020)

Rapid and Site-Specific Deep Phosphoproteome Profiling by Data-Independent Acquisition Without the Need for Spectral Libraries. (Nature Communications, 2020)

Oncogenic Mutations Rewire Signaling Pathways by Switching Protein Recruitment to Phosphotyrosine Sites. (Cell, 2019)

Integrated Proximal Proteomics Reveals IRS2 as a Determinant of Cell Survival in ALK-driven Neuroblastoma. (Science Signaling, 2018)

Prexasertib, a Cell Cycle Checkpoint Kinase 1 and 2 Inhibitor, in BRCA Wild-Type Recurrent High-Grade Serous Ovarian Cancer: a First-in-Class Proof-of Concept Phase 2 Study. (The Lancet Oncology, 2018)

Large-Scale Phosphoproteomics Reveals SHP-2 Phosphatase-Dependent Regulation of PDGF Receptor Signaling. (Cell Reports, 2018)

Phosphoproteomics of Primary Cells Reveals Druggable Kinase Signatures in Ovarian Cancer. (Cell Reports, 2017)

Development and Clinical Validation of an In Situ Biopsy-Based Multimarker Assay for Risk Stratification in Prostate Cancer. (Clinical Cancer Research, 2015)

Identification of Proteomics Biomarkers Predicting Prostate Cancer Aggressiveness and Lethality Despite Biopsy-Sampling Error. (British Journal of Cancer, 2014)

Pathway-Based Identification of Biomarkers for Targeted Therapeutics: Personalized Oncology with PI3K Pathway Inhibitors. (Science Translational Medicine, 2010)

Global, In Vivo, and Site-Specific Phosphorylation Dynamics in Signaling Networks. (Cell, 2006)

Oncogenic Kinase Signaling. (Nature, 2001)