Iambic Therapeutics has advanced its first pipeline candidate into the clinic, an AI-designed treatment for human epidermal growth factor receptor 2 (HER2)-driven cancers that moved from discovery to a first-in-human study in under two years—less than one-third of the average six-year conventional early drug development timeframe.

IAM1363 is a selective and brain-penetrant small molecule inhibitor of HER2 signaling for solid tumors. Iambic has dosed its first patient in a Phase I/Ib trial (NCT06253871) designed to evaluate the tolerability, pharmacokinetics, pharmacodynamics, and preliminary efficacy of IAM1363, both as monotherapy and in combination with trastuzumab in patients with advanced HER2 cancers. Trastuzumab is a HER2/neu receptor antagonist marketed as Herceptin® by Roche and its Genentech subsidiary.

The open-label, multi-center, dose escalation and dose optimization trial has an estimated primary completion date of April 2028.

In preclinical studies, according to Iambic, IAM1363 has shown over 1,000-fold selectivity for HER2 compared to EGFR, as well as a promising pharmacokinetic and safety profile, preferential tumor enrichment, and penetration of the central nervous system. In HER2 tumor models, including intracranial tumor models, IAM1363 demonstrated favorable efficacy and tolerability compared to benchmark tyrosine kinase inhibitors and HER2-targeted antibody-drug conjugates.

Iambic plans to present additional preclinical data for IAM1363 next week at the American Association for Cancer Research (AACR) Annual Meeting, set for April 5–10 in San Diego. That data, the company said last week, will be presented through a poster highlighting how the drug’s binding mechanism and potent HER2 activity overcomes multiple resistance mechanisms and how its EGFR avoidance is strong enough to enhance its safety.

By inhibiting HER2 wildtype and oncogenic mutant proteins, IAM1363 is designed to expand the therapeutic index—a ratio comparing the blood concentration at which a drug becomes toxic and the concentration at which it is effective—compared to available HER2 inhibitors, as well as avoid toxicities from off-target inhibition of the epidermal growth factor receptor (EGFR), a related receptor tyrosine kinase.

“Huge opportunity”

“We saw a huge unmet need and a huge opportunity associated with three things,” Tom Miller, PhD, Iambic’s co-founder and CEO, told GEN Edge. “One was expanding the efficacy versus tolerability with a more selective compound. Another was addressing more patients, including resistance mechanisms, by not only hitting HER2 wild type, but also the HER2 mutations that are disease-causing in many different areas of cancer. Then, third, being able to address the large number of patients that sadly develop brain metastases, which is a leading cause of morbidity in HER2-driven cancers.”

“We saw a very meaningful opportunity for a better drug to address those challenges, and we have one,” Miller added.

According to Iambic, IAM1363 engages the HER2 protein in a structurally distinct way from any previously known HER2 tyrosine kinase inhibitor (TKI) since it’s a type 2 TKI; all previously reported HER2 small molecule inhibitors are type 1 inhibitors.

“it’s a distinct scaffold. It leads to a novel structural engagement, and it leads to improved properties, including 10 times better selectivity for HER2 engagement versus off target tox than any other known TKI, 10 times better brain penetrance than tucatinib, as well as addressing all of those different additional mutations.

Tucatinib, marketed by Seagen as Tukysa®, is a HER2 brain penetrant TKI approved for indications that include treatment of adults with advanced unresectable or metastatic HER2-positive breast cancer (in combination with trastuzumab and capecitabine) who have received one or more prior anti-HER2-based regimens in the metastatic setting.

IAM1363 is Iambic’s first clinical candidate to be developed through its NeuralPLexer drug discovery platform, designed to identify therapeutic candidates with differentiated drug profiles by unifying physics-based machine learning and experimental automation.