9071 Background: Investigational agent APG-2449 is a novel, orally active FAK inhibitor and a third-generation ALK/ROS1 TKI that has shown potent activity against a range of ALK-resistant mutations, including G1202R, L1196M, V1180L, E1210K, S1206F, G1269A, F1174L, I1171S, and C1156Y in preclinical NSCLC, mesothelioma, and other solid tumor models. Methods: This dose escalation and dose expansion study evaluated APG-2449 in patients with second-generation TKI-resistant ALK/ROS1+ NSCLC or mesothelioma. APG-2449 was administered orally once daily at the assigned doses on a 28-day cycle using a “3+3” dose escalation design under fasted/fed conditions. Study aims were to assess safety/tolerability, recommended phase 2 dose (RP2D), pharmacokinetics (PK), pharmacodynamics (PD), and efficacy. Results: As of the data cutoff date of December 30, 2021, 84 pts (median age 52 [range 21-78] years; 42% female) with NSCLC or mesothelioma enrolled were treated with APG-2449 at doses ranging from 150 to 1,500 mg. PK analyses indicated an approximately dose-proportional increase in plasma exposure under fed conditions across dose levels tested. Cerebrospinal fluid PK analyses showed that APG-2449 was brain-penetrant. Low-fat meals increased APG-2449 Cmax and AUC by approximately 40% to 80% compared to fasting conditions. Based on PK, biomarker, efficacy, and safety results, the RP2D was determined to be 1,200 mg. Four partial responses (PRs) were observed in 14 ALK+ pts resistant to second-generation TKIs treated at the RP2D. Another pt with the G1202R mutation following alectinib treatment had tumor shrinkage of 27.9%. Among 8 pts with brain metastases, 1 complete response and 3 PRs were observed intracranially. In 10 TKI-naïve pts, the overall response rate was 80% (ALK+, 6/8; ROS1+, 2/2) and the disease control rate (DCR) was 100%. Preliminary biomarker data showed decreased FAK phosphorylation in peripheral blood mononuclear cells and increased IFN-γ levels in serum after multiple doses of APG-2449. No dose-limiting toxicity was observed. A total of 66 (78.6%) pts experienced treatment-related adverse events (TRAEs). The most frequent TRAEs included elevated blood creatinine (33.3%), ALT (25.0%), and AST (19.0%) levels and gastrointestinal disorders: nausea (22.6%), vomiting (17.9%), and diarrhea (13.1%). Only 6 (7.1%) TRAEs were grade ≥ 3. Conclusions: APG-2449 has a favorable safety and PK profile and was well tolerated in 84 subjects. Preliminary efficacy was observed in pts whose disease was resistant to second-generation TKIs, especially among those with brain metastases, and in TKI-naïve pts. Biomarker data indicated potential target engagement on FAK and immunomodulatory effects of APG-2449. Clinical trial information: NCT03917043.

In recent years, renewable energy has received extensive attention due to its advantages of sustainability, economy, and environmental protection. However, with the rapid development of renewable energy, the problem of curtailment is becoming increasingly serious. Studying the calculation method and establishing a quantitative evaluation system of renewable energy accommodation capacity are important means to solve this problem. This paper comprehensively considers the factors affecting the accommodation of renewable energy, establishes a accommodation calculation model with the maximum accommodation of renewable energy as the optimization target based on the time series production simulation method, and uses the hybrid particle swarm optimization (PSO) algorithm to solve it. The model is verified with historical data such as load, photovoltaic (PV), and wind power in a certain region throughout the year. The experimental results verify the rationality of the renewable-energy accommodation-capacity model proposed in this paper and the correctness of the theoretical analysis. The calculation results have important reference and guiding significance for the operation and control of power-grid planning and dispatching.

BackgroundVocal features have been exploited to distinguish depression from healthy controls. While there have been some claims for success, the degree to which changes in vocal features are specific to depression has not been systematically studied. Hence, we examined the performances of vocal features in differentiating depression from bipolar disorder (BD), schizophrenia and healthy controls, as well as pairwise classifications for the three disorders.MethodsWe sampled 32 bipolar disorder patients, 106 depression patients, 114 healthy controls, and 20 schizophrenia patients. We extracted i-vectors from Mel-frequency cepstrum coefficients (MFCCs), and built logistic regression models with ridge regularization and 5-fold cross-validation on the training set, then applied models to the test set. There were seven classification tasks: any disorder versus healthy controls; depression versus healthy controls; BD versus healthy controls; schizophrenia versus healthy controls; depression versus BD; depression versus schizophrenia; BD versus schizophrenia.ResultsThe area under curve (AUC) score for classifying depression and bipolar disorder was 0.5 (F-score = 0.44). For other comparisons, the AUC scores ranged from 0.75 to 0.92, and the F-scores ranged from 0.73 to 0.91. The model performance (AUC) of classifying depression and bipolar disorder was significantly worse than that of classifying bipolar disorder and schizophrenia (corrected p < 0.05). While there were no significant differences in the remaining pairwise comparisons of the 7 classification tasks.ConclusionVocal features showed discriminatory potential in classifying depression and the healthy controls, as well as between depression and other mental disorders. Future research should systematically examine the mechanisms of voice features in distinguishing depression with other mental disorders and develop more sophisticated machine learning models so that voice can assist clinical diagnosis better.

Background Targeted delivery of chemotherapeutic drugs to tumour cells is a major challenge for cancer chemotherapy. Recent studies show that tumour cell-derived microparticles can be used as vectors to package chemotherapeutic drugs, and selectively deliver drugs to tumour cells. Nevertheless, since the particle size range of microparticles is relatively wide, the sizes may exhibit different pharmacokinetic characteristics in the body, which will have a great impact on the application of drug-loaded microparticles. Here in this report, we compare the characteristics, distribution in vivo and antitumour efficacy of small microparticles (SMPs, ≤ 200 nm) and large microparticles (LMPs, > 200 nm) which loaded with methotrexate, in order to screen out more suitable carrier sizes. Results In vivo and in vitro studies have proved that the drug-loaded vesicles of SMPs (mainly 100–200 nm) are more reasonable, and the drug content and maintenance in tumour tissues. The time is significantly higher than that of LMPs (mainly 400–500 nm). At the same time, we found that SMPs can be better taken up and processed by DC cells to promote the proliferation of specific T cells. SMPs show obvious advantages in both drug delivery and immune activation, which is verified by the comparison of the efficacy of SMPs and LMPs in the treatment of solid tumours in mice. Conclusions The present data demonstrate that the SMPs had a higher cumulative concentration in tumour tissue, and the tumour suppressive effect was also significantly better than that of LMPs. It provides important process parameters for the drug-loaded vesicle delivery system. Future works will aim to expand production scale and improve the separation and purification process of the microparticles. Although the research and application of drug-loaded vesicles derived from tumour cells is still in its infancy, it has broad prospects for tumour therapy.

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