Radiation therapy with phenotypic medicine: towards N-of-1 personalization.

In current clinical practice, radiotherapy (RT) is prescribed as a pre-determined total dose divided over daily doses (fractions) given over several weeks. The treatment response is typically assessed months after the end of RT. However, the conventional one-dose-fits-all strategy may not achieve the desired outcome, owing to patient and tumor heterogeneity. Therefore, a treatment strategy that allows for RT dose personalization based on each individual response is preferred. Multiple strategies have been adopted to address this challenge. As an alternative to current known strategies, artificial intelligence (AI)-derived mechanism-independent small data phenotypic medicine (PM) platforms may be utilized for N-of-1 RT personalization. Unlike existing big data approaches, PM does not engage in model refining, training, and validation, and guides treatment by utilizing prospectively collected patient's own small datasets. With PM, clinicians may guide patients' RT dose recommendations using their responses in real-time and potentially avoid over-treatment in good responders and under-treatment in poor responders. In this paper, we discuss the potential of engaging PM to guide clinicians on upfront dose selections and ongoing adaptations during RT, as well as considerations and limitations for implementation. For practicing oncologists, clinical trialists, and researchers, PM can either be implemented as a standalone strategy or in complement with other existing RT personalizations. In addition, PM can either be used for monotherapeutic RT personalization, or in combination with other therapeutics (e.g. chemotherapy, targeted therapy). The potential of N-of-1 RT personalization with drugs will also be presented.

[Chronic hepatotoxicity evaluation of Chinese medicinal herb Zishen Yutai pill prepared from Polygoni Multiflori Radix preparata in dogs].

To study the chronic hepatotoxicity of Chinese medicine Zishen Yutai pill (ZYP) prepared from Polygonum multiflorum with the recommended dosage in normal Beagle dogs. Low, middle and high doses of ZYP (1.5, 3.0, 6.0 g·kg⁻¹; i.e. 3×, 6× and 12× equivalent doses) were given orally to dogs for 39 consecutive weeks. At the same time, the same volume of deionized water was used as the solvent control group, one time a day. The general condition of the animals was observed every day during the period of administration, and the blood was collected before and 13, 26, 39, 43 weeks after administration to detect the biomarkers related to the hepatotoxicity of the dog serum. 2/7, 3/7 and 2/7 animals were dissected after 13, 39, and 43 weeks of administration to observe the pathological changes of the animal organs, weigh the mass of main organs and conduct pathological examination of the liver. As compared to the solvent control group, 11 liver hepatotoxicity traditional biomarkers such as ALT, AST were found no ZYP-related changes at month 3, 6, 9 of the administration and month 1 in recovery period; There was no significant difference in liver viscera index and liver pathology. Therefore, no obvious hepatotoxicity was shown by ZYP administered up to 6.0 g·kg⁻¹ for 9 months in normal dogs at doses of 1.5, 3.0, and 6.0 g·kg⁻¹.

Personalized dose selection for the first Waldenström macroglobulinemia patient on the PRECISE CURATE.AI trial.

The digital revolution in healthcare, amplified by the COVID-19 pandemic and artificial intelligence (AI) advances, has led to a surge in the development of digital technologies. However, integrating digital health solutions, especially AI-based ones, in rare diseases like Waldenström macroglobulinemia (WM) remains challenging due to limited data, among other factors. CURATE.AI, a clinical decision support system, offers an alternative to big data approaches by calibrating individual treatment profiles based on that individual's data alone. We present a case study from the PRECISE CURATE.AI trial with a WM patient, where, over two years, CURATE.AI provided dynamic Ibrutinib dose recommendations to clinicians (users) aimed at achieving optimal IgM levels. An 80-year-old male with newly diagnosed WM requiring treatment due to anemia was recruited to the trial for CURATE.AI-based dosing of the Bruton tyrosine kinase inhibitor Ibrutinib. The primary and secondary outcome measures were focused on scientific and logistical feasibility. Preliminary results underscore the platform's potential in enhancing user and patient engagement, in addition to clinical efficacy. Based on a two-year-long patient enrollment into the CURATE.AI-augmented treatment, this study showcases how AI-enabled tools can support the management of rare diseases, emphasizing the integration of AI to enhance personalized therapy.

N-of-1 medicine.

ABSTRACT: The fields of precision and personalised medicine have led to promising advances in tailoring treatment to individual patients. Examples include genome/molecular alteration-guided drug selection, single-patient gene therapy design and synergy-based drug combination development, and these approaches can yield substantially diverse recommendations. Therefore, it is important to define each domain and delineate their commonalities and differences in an effort to develop novel clinical trial designs, streamline workflow development, rethink regulatory considerations, create value in healthcare and economics assessments, and other factors. These and other segments are essential to recognise the diversity within these domains to accelerate their respective workflows towards practice-changing healthcare. To emphasise these points, this article elaborates on the concept of digital health and digital medicine-enabled N-of-1 medicine, which individualises combination regimen and dosing using a patient's own data. We will conclude with recommendations for consideration when developing novel workflows based on emerging digital-based platforms.

Comparing the Performance of Multiple Small-Data Personalized Tacrolimus Dosing Models for Pediatric Liver Transplant: A Retrospective Study.

Tacrolimus is a potent immunosuppressant used after pediatric liver transplant. However, tacrolimus's narrow therapeutic window, reliance on physicians' experience for the dose titration, and intra- and inter-patient variability result in liver transplant patients falling out of the target tacrolimus trough levels frequently. Existing personalized dosing models based on the area-under-the-concentration over time curves require a higher frequency of blood draws than the current standard of care and may not be practically feasible. We present a small-data artificial intelligence-derived platform, CURATE.AI, that uses data from individual patients obtained once daily to model the dose and response relationship and identify suitable doses dynamically. Retrospective optimization using 6 models of CURATE.AI and data from 16 patients demonstrated good predictive performance and identified a suitable model for further investigations.Clinical Relevance- This study established and compared the predictive performance of 6 personalized tacrolimus dosing models for pediatric liver transplant patients and identified a suitable model with consistently good predictive performance based on data from pediatric liver transplant patients.

The alteration of inflammatory markers and apoptosis on chronic prostatitis induced by estrogen and androgen.

PURPOSE: The age-related decline of the testosterone-to-estrogen (T-to-E2) ratio in serum is associated with the increased prevalence of prostatic inflammation. The goal of the study was to induce prostatic inflammation with E2 and androgen treatment and to explore the inflammatory markers and apoptosis on prostatitis. METHODS: Castrated SD rats were treated with E2 and different doses of androgens to achieve an elevated concentration of E2 and a wide range of the androgen-to-E2 ratio in serum. Inflammatory markers TNF-α, COX-2 and MIP-1α were immunohistochemically stained. Apoptosis detection was evaluated by TUNEL staining. E2, T and DHT concentrations in serum were measured, and the relative weight of the prostate and seminal vesicles were determined. RESULTS: T was anti-inflammatory at the doses which normalized or over stimulated the growth of the prostate and seminal vesicles. Experimentally, prostatitis induced by E2 alone increased the prostatic levels of the inflammatory markers TNF-a, COX-2 and MIP-1a. As signs of anti-estrogenic actions, androgens dose-dependently decreased the expression of TNF-α, COX-2 and MIP-1α. Prostatitis induced by E2 alone caused extensive apoptosis in the castrate-resistant cells and E2-induced apoptosis occurred dependently of T manipulation. CONCLUSIONS: Estrogen-alone-induced inflammatory response could promote the expression of inflammatory markers; however, T supplementation reduces the expression of inflammatory markers and E2-induced apoptosis occurs dependently on T manipulation in prostatitis.