Lantern Pharma has dosed two subjects in a Phase Ia/Ib clinical trial of its investigational drug LP-284 for relapsed or refractory non-Hodgkin’s lymphoma (NHL).
A new small molecule, LP-284 has a synthetically lethal mechanism of action that kills cancer cells harbouring mutations in DNA damage repair pathways.
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The multicentre, open-label trial aims to determine the safety and tolerability of varying doses of the molecule.
LP-284 will be assessed for its potential to treat mantle cell lymphoma (MCL), double hit lymphoma (DHL), high-grade B-cell lymphomas (HGBL), and selected solid tumours and sarcomas.
The trial will also investigate the maximum tolerated dose (MTD) and the recommended Phase 2 dose (RP2D) in patients with relapsed or refractory (r/r) lymphomas and solid tumours.
Characterising the pharmacokinetics (PK) of LP-284 and evaluating its clinical activity are the trial’s secondary objectives.
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By GlobalDataPatients in the trial will receive LP-284 intravenously on days one, eight, and 15 of a 28-day cycle.
The study is structured in two parts: a dose escalation portion to confirm MTD and/or RP2D (Phase Ia) and dose expansion (Phase Ib).
Lantern plans to enrol up to 30 subjects in Phase Ia portion with the total number depending on the number of dose levels assessed.
Phase Ib portion could enrol up to 40 subjects in each cohort for MCL and HGBL, including diffuse large B-cell lymphoma (DLBCL) tumours.
Lantern president and CEO Panna Sharma said: “Enrolling and treating initial patients in our Phase I trial for LP-284 is a major milestone and underscores the commitment of our team to advancing our pipeline of AI-driven therapies to patients.
“We believe LP-284 has unique and breakthrough potential for patients with relapsed or refractory lymphomas and certain solid tumours with certain genomic signatures, many of which have no or limited effective therapeutic options.”
“Insights from our proprietary AI and ML platform, RADR, were instrumental in our development of LP-284 and aided in understanding its mechanism of action, identifying and prioritising its cancer indications, and generating machine learning biomarker signatures to assist with patient selection in future clinical trials.”
