When Medicine Meets Engineering-Paradigm Shifts in Diagnostics and Therapeutics

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

The clinical team attending to a patient upon a diagnosis is faced with two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions upon. However, individuals rarely demonstrate the reported response from relevant clinical trials, often the average from a group representing a population or subpopulation. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies over time with the changes in his or her condition, whether via the indication or physiology. In practice, the drug and the dose selection depend greatly on the medical protocol of the healthcare provider and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data approaches have emerged as an excellent decision-making support tool, but their application is limited by multiple challenges, the main one being the availability of sufficiently big datasets with good quality, representative information. An alternative approach-phenotypic personalized medicine (PPM)-finds an appropriate drug combination (quadratic phenotypic optimization platform [QPOP]) and an appropriate dosing strategy over time (CURATE.AI) based on small data collected exclusively from the treated individual. PPM-based approaches have demonstrated superior results over the current standard of care. The side effects are limited while the desired output is maximized, which directly translates into improving the length and quality of individuals' lives.

Mechanism-independent optimization of combinatorial nanodiamond and unmodified drug delivery using a phenotypically driven platform technology

Combination chemotherapy can mediate drug synergy to improve treatment efficacy against a broad spectrum of cancers. However, conventional multidrug regimens are often additively determined, which have long been believed to enable good cancer-killing efficiency but are insufficient to address the nonlinearity in dosing. Despite improved clinical outcomes by combination treatment, multi-objective combination optimization, which takes into account tumor heterogeneity and balance of efficacy and toxicity, remains challenging given the sheer magnitude of the combinatorial dosing space. To enhance the properties of the therapeutic agents, the field of nanomedicine has realized novel drug delivery platforms that can enhance therapeutic efficacy and safety. However, optimal combination design that incorporates nanomedicine agents still faces the same hurdles as unmodified drug administration. The work reported here applied a powerful phenotypically driven platform, termed feedback system control (FSC), that systematically and rapidly converges upon a combination consisting of three nanodiamond-modified drugs and one unmodified drug that is simultaneously optimized for efficacy against multiple breast cancer cell lines and safety against multiple control cell lines. Specifically, the therapeutic window achieved from an optimally efficacious and safe nanomedicine combination was markedly higher compared to that of an optimized unmodified drug combination and nanodiamond monotherapy or unmodified drug administration. The phenotypically driven foundation of FSC implementation does not require any cellular signaling pathway data and innately accounts for population heterogeneity and nonlinear biological processes. Therefore, FSC is a broadly applicable platform for both nanotechnology-modified and unmodified therapeutic optimizations that represent a promising path toward phenotypic personalized medicine.

A CMOS wireless biomolecular sensing system-on-chip based on polysilicon nanowire technology

As developments of modern societies, an on-field and personalized diagnosis has become important for disease prevention and proper treatment. To address this need, in this work, a polysilicon nanowire (poly-Si NW) based biosensor system-on-chip (bio-SSoC) is designed and fabricated by a 0.35 μm 2-Poly-4-Metal (2P4M) complementary metal-oxide-semiconductor (CMOS) process provided by a commercialized semiconductor foundry. Because of the advantages of CMOS system-on-chip (SoC) technologies, the poly-Si NW biosensor is integrated with a chopper differential-difference amplifier (DDA) based analog-front-end (AFE), a successive approximation analog-to-digital converter (SAR ADC), and a microcontroller to have better sensing capabilities than a traditional Si NW discrete measuring system. In addition, an on-off key (OOK) wireless transceiver is also integrated to form a wireless bio-SSoC technology. This is pioneering work to harness the momentum of CMOS integrated technology into emerging bio-diagnosis technologies. This integrated technology is experimentally examined to have a label-free and low-concentration biomolecular detection for both Hepatitis B Virus DNA (10 fM) and cardiac troponin I protein (3.2 pM). Based on this work, the implemented wireless bio-SSoC has demonstrated a good biomolecular sensing characteristic and a potential for low-cost and mobile applications. As a consequence, this developed technology can be a promising candidate for on-field and personalized applications in biomedical diagnosis.