Multiple myeloma is an incurable hematological malignancy that relies on drug combinations for first and secondary lines of treatment. The inclusion of proteasome inhibitors, such as bortezomib, into these combination regimens has improved median survival. Resistance to bortezomib, however, is a common occurrence that ultimately contributes to treatment failure, and there remains a need to identify improved drug combinations. We developed the quadratic phenotypic optimization platform (QPOP) to optimize treatment combinations selected from a candidate pool of 114 approved drugs. QPOP uses quadratic surfaces to model the biological effects of drug combinations to identify effective drug combinations without reference to molecular mechanisms or predetermined drug synergy data. Applying QPOP to bortezomib-resistant multiple myeloma cell lines determined the drug combinations that collectively optimized treatment efficacy. We found that these combinations acted by reversing the DNA methylation and tumor suppressor silencing that often occur after acquired bortezomib resistance in multiple myeloma. Successive application of QPOP on a xenograft mouse model further optimized the dosages of each drug within a given combination while minimizing overall toxicity in vivo, and application of QPOP to ex vivo multiple myeloma patient samples optimized drug combinations in patient-specific contexts.

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In 2019/2020, the emergence of coronavirus disease 2019 (COVID-19) resulted in rapid increases in infection rates as well as patient mortality. Treatment options addressing COVID-19 included drug repurposing, investigational therapies such as remdesivir, and vaccine development. Combination therapy based on drug repurposing is among the most widely pursued of these efforts. Multi-drug regimens are traditionally designed by selecting drugs based on their mechanism of action. This is followed by dose-finding to achieve drug synergy. This approach is widely-used for drug development and repurposing. Realizing synergistic combinations, however, is a substantially different outcome compared to globally optimizing combination therapy, which realizes the best possible treatment outcome by a set of candidate therapies and doses toward a disease indication. To address this challenge, the results of Project IDentif.AI (Identifying Infectious Disease Combination Therapy with Artificial Intelligence) are reported. An AI-based platform is used to interrogate a massive 12 drug/dose parameter space, rapidly identifying actionable combination therapies that optimally inhibit A549 lung cell infection by vesicular stomatitis virus within three days of project start. Importantly, a sevenfold difference in efficacy is observed between the top-ranked combination being optimally and sub-optimally dosed, demonstrating the critical importance of ideal drug and dose identification. This platform is disease indication and disease mechanism-agnostic, and potentially applicable to the systematic N-of-1 and population-wide design of highly efficacious and tolerable clinical regimens. This work also discusses key factors ranging from healthcare economics to global health policy that may serve to drive the broader deployment of this platform to address COVID-19 and future pandemics.

Supportinginformation and the ORCID identification number(s) for the author(s) of this article can be found under: https://doi.

BACKGROUND & AIMS: Some oncogenes encode transcription factors, but few drugs have been successfully developed to block their activity specifically in cancer cells. The transcription factor SALL4 is aberrantly expressed in solid tumor and leukemia cells. We developed a screen to identify compounds that reduce the viability of liver cancer cells that express high levels of SALL4, and we investigated their mechanisms. METHODS: We developed a stringent high-throughput screening platform comprising unmodified SNU-387 and SNU-398 liver cancer cell

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to multiple drug repurposing clinical trials that have yielded largely uncertain outcomes. To overcome this challenge, we used IDentif.AI, a platform that pairs experimental validation with artificial intelligence (AI) and digital drug development to rapidly pinpoint unpredictable drug interactions and optimize infectious disease combination therapy design with clinically relevant dosages. IDentif.AI was paired with a 12-drug candidate therapy set representing over 530,000 drug combinations against the SARS-CoV-2 live virus collected from a patient sample. IDentif.AI pinpointed the optimal combination as remdesivir, ritonavir, and lopinavir, which was experimentally validated to mediate a 6.5-fold enhanced efficacy over remdesivir alone. Additionally, it showed hydroxychloroquine and azithromycin to be relatively ineffective. The study was completed within 2 weeks, with a three-order of magnitude reduction in the number of tests needed. IDentif.AI independently mirrored clinical trial outcomes to date without any data from these trials. The robustness of this digital drug development approach paired with in vitro experimentation and AI-driven optimization

Near‐infrared (NIR) activatable upconversion nanoparticles (UCNPs) enable wireless‐based phototherapies by converting deep‐tissue‐penetrating NIR to visible light. UCNPs are therefore ideal as wireless transducers for photodynamic therapy (PDT) of deep‐sited tumors. However, the retention of unsequestered UCNPs in tissue with minimal options for removal limits their clinical translation. To address this shortcoming, biocompatible UCNPs implants are developed to deliver upconversion photonic properties in a flexible, optical guide design. To enhance its translatability, the UCNPs implant is constructed with an FDA‐approved poly(ethylene glycol) diacrylate (PEGDA) core clad with fluorinated ethylene propylene (FEP). The emission spectrum of the UCNPs implant can be tuned to overlap with the absorption spectra of the clinically relevant photosensitizer, 5‐aminolevulinic acid (5‐ALA). The UCNPs implant can wirelessly transmit upconverted visible light till 8 cm in length and in a bendable manner even when implanted underneath the skin or scalp. With this system, it is demonstrated that NIR‐based chronic PDT is achievable in an untethered and noninvasive manner in a mouse xenograft glioblastoma multiforme (GBM) model. It is postulated that such encapsulated UCNPs implants represent a translational shift for wireless deep‐tissue phototherapy by enabling sequestration of UCNPs without compromising wireless deep‐tissue light delivery.