Sharp Therapeutics announces $3 million convertible note offering

February 2, 2026 12:11 PM EST

Sharp Therapeutics Corp. (TSXV: SHRX) (OTCQB: SHRXF) announced a non-brokered private placement for up to $3.0 million in unsecured convertible notes. The Pittsburgh and Toronto-based company expects the initial closing to occur around February 12, 2026.



Each note will have a principal amount of $1,000 and bear 6% annual interest. The notes can be converted into common shares at $2.00 per share at any time before the 12-month maturity date. The company will use proceeds for general working capital purposes.



The notes include mandatory conversion provisions if Sharp completes an equity financing of at least $7 million, excluding conversion proceeds from the notes. In such cases, the company must provide holders 20 days advance notice before converting all outstanding principal amounts.



STX Partners LLC, Newlin Investment Company 1 LLC, and other company insiders have confirmed their participation in the note offering.



Sharp also confirmed details of a previously announced common share offering at $2.50 per share, expected to close in the first quarter of 2026. STX Partners, the company's largest shareholder, committed to purchase at least 1.6 million common shares for approximately $4 million, contingent on the company raising at least $6 million from other investors and closing by March 15, 2026.



Both offerings constitute related-party transactions due to insider participation. The company claims exemption from formal valuation and minority shareholder approval requirements under securities regulations, stating the transaction value will not exceed 25% of market capitalization.



All securities issued will be subject to a four-month and one-day hold period and require TSX Venture Exchange approval. No finder's fees are payable for either offering.



Sharp Therapeutics is a preclinical-stage company developing small-molecule therapeutics for genetic diseases using high-throughput screening technologies and computational optimization.


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