Aerosol-Stable Peptide-Coated Liposome Nanoparticles: A Proof-of-Concept Study with Opioid Fentanyl in Enhancing Analgesic Effects and Reducing Plasma Drug Exposure

John D. Hoekman; Pramod Srivastava; Rodney J.Y. Ho

doi:10.1002/jps.24022

Proof-of-concept study: Homomorphically encrypted data can support real-time learning in personalized cancer medicine

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-019-0983-9 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 1

Author(s):

Silvia Paddock ◽

Hamed Abedtash ◽

Jacqueline Zummo ◽

Samuel Thomas

Keyword(s):

Real World ◽

Drug Exposure ◽

Homomorphic Encryption ◽

Unmet Need ◽

Learning System ◽

Administrative Claims ◽

Use Case ◽

Proof Of Concept ◽

Cancer Treatments ◽

Concept Study

Abstract Background The successful introduction of homomorphic encryption (HE) in clinical research holds promise for improving acceptance of data-sharing protocols, increasing sample sizes, and accelerating learning from real-world data (RWD). A well-scoped use case for HE would pave the way for more widespread adoption in healthcare applications. Determining the efficacy of targeted cancer treatments used off-label for a variety of genetically defined conditions is an excellent candidate for introduction of HE-based learning systems because of a significant unmet need to share and combine confidential data, the use of relatively simple algorithms, and an opportunity to reach large numbers of willing study participants. Methods We used published literature to estimate the numbers of patients who might be eligible to receive treatments approved for other indications based on molecular profiles. We then estimated the sample size and number of variables that would be required for a successful system to detect exceptional responses with sufficient power. We generated an appropriately sized, simulated dataset (n = 5000) and used an established HE algorithm to detect exceptional responses and calculate total drug exposure, while the data remained encrypted. Results Our results demonstrated the feasibility of using an HE-based system to identify exceptional responders and perform calculations on patient data during a hypothetical 3-year study. Although homomorphically encrypted computations are time consuming, the required basic computations (i.e., addition) do not pose a critical bottleneck to the analysis. Conclusion In this proof-of-concept study, based on simulated data, we demonstrate that identifying exceptional responders to targeted cancer treatments represents a valuable and feasible use case. Past solutions to either completely anonymize data or restrict access through stringent data use agreements have limited the utility of abundant and valuable data. Because of its privacy protections, we believe that an HE-based learning system for real-world cancer treatment would entice thousands more patients to voluntarily contribute data through participation in research studies beyond the currently available secondary data populated from hospital electronic health records and administrative claims. Forming collaborations between technical experts, physicians, patient advocates, payers, and researchers, and testing the system on existing RWD are critical next steps to making HE-based learning a reality in healthcare.

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A mechanism-based proof of concept study on the effects of duloxetine in patients with painful knee osteoarthritis

Trials ◽

10.1186/s13063-021-05941-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Nadia Ammitzbøll ◽

Lars Arendt-Nielsen ◽

Davide Bertoli ◽

Christina Brock ◽

Anne Estrup Olesen ◽

...

Keyword(s):

Analgesic Effect ◽

Mechanistic Study ◽

Proof Of Concept ◽

Pain Mechanisms ◽

Concept Study ◽

Analgesic Effects ◽

Link Type ◽

Period 2 ◽

Pressure Pain Thresholds ◽

Underlying Mechanisms

Abstract Background The global burden of osteoarthritis (OA) is steadily increasing due to demographic and lifestyle changes. The nervous system can undergo peripheral and central neuroplastic changes (sensitization) in patients with OA impacting the options to manage the pain adequately. As a result of sensitization, patients with OA show lower pressure pain thresholds (PPTs), facilitated temporal summation of pain (TSP), and impaired conditioned pain modulation (CPM). As traditional analgesics (acetaminophen and non-steroidal anti-inflammatory drugs) are not recommended for long-term use in OA, more fundamental knowledge related to other possible management regimes are needed. Duloxetine is a serotonin-noradrenalin reuptake inhibitor, and analgesic effects are documented in patients with OA although the underlying fundamental mechanisms remain unclear. The descending pain inhibitory control system is believed to be dependent on serotonin and noradrenalin. We hypothesized that the analgesic effect of duloxetine could act through these pathways and consequently indirectly reduce pain and sensitization. The aim of this mechanistic study is to investigate if PPTs, TSP, CPM, and clinical pain parameters are modulated by duloxetine. Methods This proof of concept study is a randomized, placebo-controlled, double-blinded, crossover trial, which compares PPTs, TSP, and CPM before and after 18 weeks of duloxetine and placebo in forty patients with knee OA. The intervention periods include a titration period (2 weeks), treatment period (60 mg daily for 14 weeks), and a discontinuation period (2 weeks). Intervention periods are separated by 2 weeks. Discussion Duloxetine is recommended for the treatment of chronic pain, but the underlying mechanisms of the analgesic effects are currently unknown. This study will investigate if duloxetine can modify central pain mechanisms and thereby provide insights into the underlying mechanisms of the analgesic effect. Trial registration ClinicalTrials.govNCT04224584. Registered on January 6, 2020. EudraCT 2019-003437-42. Registered on October 22, 2019. The North Denmark Region Committee on Health Research Ethics N-20190055. Registered on October 31, 2019.

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