Instructional Biology files patent for native biologics platform

Instructional Biology said founder Boris Markosian filed a U.S. patent application in September 2026 for a biotechnology platform built around natural biologics, AI-guided discovery and digital twin-driven therapeutics. The company says the research-stage system is meant to help manufacture fragile biologics more safely and tailor treatments to individual patients.

Why it matters: - Instructional Biology is trying to shift drug development away from synthetic chemistry and toward biologics the body already makes. - The platform is designed to address manufacturing bottlenecks that can damage fragile proteins and other natural therapeutic signals. - If the approach works, it could support more personalized treatments and new ways to match therapies to individual patients.

What happened: - Founder and sole inventor Boris Markosian filed a U.S. patent application for a biotechnology platform in September 2026. - The application, No. 19/793,487, was filed with the U.S. Patent and Trademark Office. - Instructional Biology said the filing builds on five previously filed provisional patent applications by Markosian. - The company based the filing on an integrated platform of five claim families.

The details: - The application is titled "Instructional Biology Systems Using an Advanced Science AI Platform and the Markosian High-Fidelity Bio-Processing Protocol." - The platform combines AI discovery, bio-manufacturing, laser pathogen reduction, exosome delivery and digital twin-guided therapeutics. - The bio-manufacturing protocol is designed to keep delicate biologics at temperatures below the point where the most fragile proteins begin to unravel. - The process monitors molecular conformation continuously to help keep biologics in their native state. - The application describes precisely tuned lasers meant to reduce pathogens without crossing the energy needed to break peptide bonds. - The Advanced Science AI Platform, or ASAP, maps biological signaling as a web of connections and suggests which signals may matter for a given condition. - Markosian described ASAP as a way to move beyond the "flatland" of traditional AI by modeling biology in a shape that better fits its hierarchy. - Core claims cover a cyber-physical bioprocessing system, an optical pathogen-reduction apparatus, an exosome carrier-manufacturing system, a computer-implemented system for identifying candidate therapeutic interventions and a patient-specific therapeutic-regulation system based on digital twins. - The application describes potential uses in postpartum depression, pain, opioid-related conditions, wound healing and skin aging. - The filing also describes tailoring treatments to individual patients through the ASAP platform using digital twins. - Instructional Biology said the technology is at the research and development stage and has not been approved by any regulatory agency. - The company said the announcement is not a claim of clinical efficacy or medical advice. - More information is available in the founder page.

Between the lines: - The filing is as much a systems play as a drug-ingredient play, tying discovery, manufacturing and patient matching into one platform. - The emphasis on native biologics suggests the company sees unmet value in preserving biological form, not just delivering an active ingredient. - The patent framing also signals a long development timeline, with Markosian saying the effort took 15 years to reach this point. - The company is positioning the work as foundational research rather than a near-term product launch.

What's next: - Instructional Biology said it is seeking research partners, including an independent laboratory to begin validating the platform. - The company is also welcoming inquiries from prospective partners and investors. - Markosian said the patent filing is a starting line, and the next step is rigorous science to test what the platform can do.

The bottom line: - Instructional Biology is betting that medicine built around the body's own signals, paired with AI and new manufacturing methods, can overcome some limits of conventional drug development.