Academic literature on the topic 'Sleep cycle - drug research'

AbstractMelatonin is a hormone produced by the pineal gland. In addition to its hormonal effect, it has strong antioxidant properties. Melatonin is probably best known for its ability to control circadian rhythm; it is sold in many countries as a supplement or drug for improving of sleep quality. However, melatonin’s effect is not limited to control of circadian rhythm:. it is involved in other effects, including cell cycle control and regulation of several important enzymes, including inhibition of inducible nitric oxide synthase. Melatonin affects immunity as well. It can modulate the immune response on disparate levels with a significant effect on inflammation. The role of melatonin in body regulatory process is not well understood; only limited conclusions can be drawn from known data. The current review attempts to summarize both basic facts about melatonin’s effects and propose research on the lesser known issues in the future.

e19332 Background: US MM-6 is investigating iCT from parenteral V-based induction to all-oral I-lenalidomide-dexamethasone (IRd) with the aim of increasing proteasome inhibitor (PI)-based treatment adherence/duration while maintaining quality of life (QoL) & further improving outcomes in the diverse US community population. Methods: 21 US community sites, including VA hospitals, are enrolling non-transplant-eligible newly diagnosed MM pts with ≥stable disease after 3 cycles of V-based therapy to receive IRd. Pts use mobile & digital devices to collect actigraphy (activity/sleep) data, complete QoL & treatment satisfaction questionnaires, & self-report medication adherence. Primary endpoint: progression-free survival (PFS); key secondary endpoints include: response rates & duration of therapy. Results: As of Nov 18, 2019, 84 pts had been treated (median age 73 [range 49–90] yrs; 44% ≥75 yrs; 49% male; 15% black/African American; 10% Hispanic/Latino; 35% International Staging System stage III disease; 42% lytic bone disease). Comorbidities included hypertension (57%), anemia (44%), fatigue (43%), & peripheral neuropathy (13%). 85% of pts were receiving VRd at the time of iCT. 62% of pts are still on therapy & enrollment is ongoing. After initiating iCT, ≥complete response (CR) rate increased from 4% to 26% (Table). At 8 mos median follow-up, 6 pts had progressed & there were 2 on-study deaths. The 12-mo PFS rate was 86% (95% CI, 73–93) from the start of V-based regimen & from the start of IRd. During IRd treatment, 92% of pts had treatment-emergent adverse events (TEAEs) (48% grade [G] ≥3). G3 TEAEs (≥5% of pts) were diarrhea (7%), pneumonia (6%), syncope (6%), & anemia (5%). TEAEs led to study drug discontinuation in 7% of pts; 36% had serious TEAEs. I/R/d dose was adjusted due to AEs in 39%/39%/29% of pts. Medication adherence (cycles 1–5) was ‘excellent’/‘very good’ in ≥78% of pts reporting adherence. QoL/treatment satisfaction were maintained in pts completing questionnaires. Actigraphy data showed normal activity levels & sleep durations. Conclusions: US MM-6 pts are representative of the RW US MM population & results show that iCT to an oral PI may permit prolonged PI-based therapy with promising efficacy & without impacting pts’ QoL or treatment satisfaction. Clinical trial information: NCT03173092 . [Table: see text]

6596 Background: Knowing which factors compromise quality of life (QoL) in patients undergoing cancer treatments can help oncologists provide more effective care. To identify these factors, we conducted a single-centered cross-sectional study examining the relationships between patient-reported QoL, adverse events (AE), and treatment characteristics. Methods: Consecutive patients attending an outpatient chemotherapy unit completed two questionnaires (EORTC QLQ-C30 and National Cancer Institute PRO-CTCAE) per visit to identify factors contributing to the lowest global QoL score [QLQ-C30 QL2, range 0 (worst)–100 (best)] over a 6-week period. QL2 was correlated to each PRO-CTCAE item and treatment characteristic (tumor type, drug class, number of cycles, and treatment intent) using multiple regression, adjusted for age, sex, and use of concurrent radiotherapy. To determine whether QoL can be reliably modeled by machine learning, ten algorithms were compared for performance in classifying patients into dichotomized QL2 subgroups. Results: One hundred and fifteen of 130 patients (157/244 visits) completed up to 6 sets of questionnaires (median QL2: 67, IQR: 50–83). No difference was found between QL2 and treatment characteristics (at α Bonferroni=5×10-4). However, QL2 was significantly associated with AE in gastrointestinal, respiratory, attention, pain, sleep/wake, and mood categories. Using AE as covariates, support vector machine with radial basis kernel was the best at classifying patients into QoL groups (mean bootstrapped area under ROC curve 0.812, 95% CI 0.700–0.925). Conclusions: Patient-reported QoL is associated with multiple AE, but not with characteristics of systemic therapy. Machine learning analysis suggests that a combined AE analysis may reliably characterize a patient’s QoL. [Table: see text]

Abstract Introduction: Rigosertib is a small molecule inhibitor of cellular signaling pathways in cancer cells by acting as a Ras mimetic. The inhibitory effect is mediated by Rigosertib binding to the Ras-binding domain found in many Ras effector proteins (Athuluri-Divakar SK, Cell 2016). Oral administration of Rigosertib was initially evaluated in lower risk MDS patients. The drug, administered as 560 mg BID (q12hr; 2/3 wks), was associated with high rates of transfusion independence but with significant GU AEs (Raza, et al, Blood 2017 130:1689). Subsequently, a reduction in the PM dose to 280 mg led to a decrease in GU AEs, suggesting a causal relationship with nocturnal bladder drug concentration. When oral administration of Rigosertib was tested with standard dose of parenteral Azacitidine in a Phase I/II trial (NCT01926587) at a dose of 560/280 mg (Q12hrs, 3/4 weeks), the ORR was 77%; with 60% for the HMA relapsed/refractory group for the high risk MDS patients. The impressive response rate, in the combination treatment, was also associated with significant GU AEs. Hence, it is important to understand the underlying cause and devise ways to maximize response rates with minimization of GU AEs. Previously, it was established that GU AEs were unlikely to be related to the higher systemic exposure of the drug (Maniar, et al ASH 2018, Abstract submitted) but attributed to the nocturnal dwell time of high drug concentration in the bladder of patients treated with continuous oral administration (3/4 weeks). In this investigation, we developed a pharmacokinetic model to simulate the systemic and bladder exposure of Rigosertib after repeated oral dosing. The overall aim was to identify and implement an oral dosing regimen for Rigosertib that would maximize the systemic exposure with minimization of nocturnal bladder concentration, thereby potentially mitigating or eliminating the GU AE's. Methods: A 2-compartment model with 1st order absorption and elimination (Figure 1) was fitted to data collected from patients with solid malignancies at a 560 mg dose (n=25). Model parameter estimates were then used to generate a virtual population of 100 patients. Each virtual patient was randomly assigned parameter values based on the 95% confidence interval for each parameter estimate obtained through the modeling analysis. Model simulations were then performed to evaluate the steady state systemic and urinary exposure of Rigosertib in the virtual population after treatment with different dosing schedules (Figure 2). Besides plasma exposure (Cmax, AUC), the predicted rigosertib bladder concentration during the sleep-cycle was also assessed. From this analysis, optimal dosing regimens were selected for evaluation in a Phase 2 study in HR-MDS patients in combination with Azacitidine. The model was validated by comparing the predicted systemic exposure with observed data using these optimal dosing regimens, and by comparing Grade 3 GU AE's events from the pre- and post-optimization of dosing regimen (NCT01926587). Results: The model-predicted steady state plasma concentration-time profile of Rigosertib for different dosing regimens is shown in Figure 3. The duration of exposure of drug above minimum effective concentration (MEC, 0.175 ug/ml) was not changed by varying the dosing regimen. Importantly, model simulations demonstrated that BID dosing, with the dosing interval of 8 hours, would reduce the bladder concentration of Rigosertib by as much as 70% during sleep without compromising systemic drug exposure (Table 1). As illustrated in Table 2, model simulated Rigosertib exposure (Cmax, AUC) compared favorably with data from patients treated with the novel twice daily dosing regimens (560mg/560mg and 840 mg/280mg, dosing interval of 8 hours), thereby validating the model. Preliminary safety data from the on-going trial demonstrates that the Grade 3 GU AEs were significantly reduced (12%) with the optimized dosing regimen compared to the pre-optimized dosing regimen (29%) despite using a higher total dose of drug (1120 mg vs 840 mg). Conclusions: This study demonstrates the utility of pharmacokinetic modeling for designing a dosing regimen directed at reducing the incidence of toxicity. The identified dosing regimen, along with mitigation strategies, successfully reduced the risk of Grade 3 GU AEs of rigosertib without compromising the duration of systemic exposure of Rigosertib above MEC in HR MDS patients. Disclosures Taft: Onconova Therapeutics, Inc: Research Funding. Ren:Onconova Therapeutics, Inc: Employment, Equity Ownership. Zbyszewski:Onconova Therapeutics, Inc: Employment, Equity Ownership. Morgan:Onconova Therapeutics, Inc: Consultancy. Petrone:Onconova Terapeutics Inc.: Employment, Equity Ownership. Fruchtman:Onconova Therapeutic Inc: Employment, Equity Ownership. Maniar:Onconova Therapeutics, Inc: Employment, Equity Ownership.