A new approach examines how individual cells respond to drugs, aiming to identify risks earlier in development.
Updated
April 15, 2026 6:01 PM
Close up of a capsule blister pack. PHOTO: UNSPLASH
DeepCyte, a startup in the drug development space, is focusing on a long-standing problem: why drugs that appear safe in early testing still fail in clinical trials or are withdrawn later due to toxicity. DeepCyte has launched with US$1.5 million in seed funding to build tools that detect and explain the harmful effects of drugs at much earlier stages.
The startup’s approach focuses on how individual cells respond to a drug. Instead of analysing cells in bulk, it studies them one by one. This helps capture differences in how cells react, which are often missed in traditional testing methods.
Drug toxicity remains one of the main reasons for failure in drug development. Methods such as animal testing and bulk cell analysis do not always reflect how human cells behave. This gap has pushed the industry to look for more reliable and human-relevant ways to test drug safety.
DeepCyte combines cell-level data with artificial intelligence. Its platform, MetaCore, studies what is happening inside individual cells by capturing detailed molecular information. This data is used to build large datasets that can train AI models.
Additionally, the company has developed an AI system called DeeImmuno. It is designed to predict whether a drug could be toxic and identify the biological reasons behind it. In internal testing on 100 drugs, the system identified different types of toxicity and their underlying mechanisms with a reported accuracy of 94 percent.
The focus on explaining why a drug is toxic, not just whether it is, reflects a broader shift in the industry. Regulators such as the U.S. Food and Drug Administration and the European Medicines Agency have been encouraging methods that rely more on human cell data and clearer biological evidence. The seed funding will be used to develop and scale these tools. The company aims to help drug developers make earlier decisions, which could reduce costly failures in later stages. Whether tools like this become widely used will depend on how they perform in real-world settings. For now, DeepCyte’s approach highlights a growing effort to make drug testing more precise by focusing on how drugs affect cells at the most detailed level.
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With Phia’s AI, the new luxury is knowing what’s worth buying
Updated
February 10, 2026 12:56 PM
Phoebe Gates and Sophia Kianni, founders of Phia. PHOTO: PHIA
AI has transformed how we shop—predicting trends, powering virtual try-ons and streamlining fashion logistics. Yet some of the biggest pain points remain: endless scrolling, too many tabs and never knowing if you’ve overpaid. That’s the gap Phia aims to close.
Co-founded by Phoebe Gates, daughter of Bill Gates, and climate activist Sophia Kianni, Phia was born in a Stanford dorm room and launched in April 2025. The app, available on mobile and as a browser extension, compares prices across over 40,000 retailers and thrift platforms to show what an item really costs. Its hallmark feature, “Should I Buy This?”, instantly flags whether something is overpriced, fair or a genuine deal.
The mission is simple: make shopping smarter, fairer and more sustainable. In just five months, Phia has attracted more than 500,000 users, indexed billions of products and built over 5,000 brand partnerships. It also secured a US$8 million seed round led by Kleiner Perkins, joined by Hailey Bieber, Kris Jenner, Sara Blakely and Sheryl Sandberg—investors who bridge tech, retail and culture. “Phia is redefining how people make purchase decisions,” said Annie Case, partner at Kleiner Perkins.
Phia’s AI engine scans real-time data from more than 250 million products across its network, including Vestiaire Collective, StockX, eBay and Poshmark. Beyond comparing prices, the app helps users discover cheaper or more sustainable options by displaying pre-owned items next to new ones—helping users see the full spectrum of choices before they buy. It also evaluates how different brands perform over time, analysing how well their products hold resale value. This insight helps shoppers judge whether a purchase is likely to last in value or if opting for a second-hand version makes more sense. The result is a platform that naturally encourages circular shopping—keeping items in use longer through resale, repair or recycling—and resonates strongly with Gen Z and millennial values of sustainability and mindful spending.
By encouraging transparency and smarter choices, Phia signals a broader shift in consumer technology: one where AI doesn’t just automate decisions but empowers users to understand them. Instead of merely digitizing the act of shopping, Phia embodies data-driven accountability—using intelligent search to help consumers make informed and ethical choices in markets long clouded by complexity. Retail analysts believe this level of visibility could push brands to maintain accurate and competitive pricing. Skeptics, however, argue that Phia must evolve beyond comparison to create emotional connection and loyalty. Still, one fact stands out: algorithms are no longer just recommending what we buy—they’re rewriting how we decide.
With new funding powering GPU expansion and advanced personalization tools, Phia’s next step is to build a true AI shopping agent—one that helps people buy better, live smarter and rethink what it means to shop with purpose.