This is the first report of using microdose PK and xenograft PK/PD model to predict efficacious doses before the first-in-human trial in cancer patients. In addition, this work highlights the importance of integration of all of information in PK/PD analysis and illustrates how modeling and simulation can be used to add value in the early stages of drug development.

A liquid chromatography-triple quadrupole mass spectrometric (LC-MS/MS) method was developed and validated for the determination of 5-nitro-5'-hydroxy-indirubin-3'-oxime (AGM-130) in human plasma to support a microdose clinical trial. The method consisted of a liquid-liquid extraction for sample preparation and LC-MS/MS analysis in the positive ion mode using TurboIonSpray(TM) for analysis. d3 -AGM-130 was used as the internal standard. A linear regression (weighted 1/concentration) was used to fit calibration curves over the concentration range of 10-2000 pg/mL for AGM-130. There were no endogenous interference components in the blank human plasma tested. The accuracy at the lower limit of quantitation was 96.6% with a precision (coefficient of variation, CV) of 4.4%. For quality control samples at 30, 160 and 1600 pg/mL, the between run CV was ≤5.0 %. Between-run accuracy ranged from 98.1 to 101.0%. AGM-130 was stable in 50% acetonitrile for 168 h at 4°C and 6 h at room temperature. AGM-130 was also stable in human plasma at room temperature for 6 h and through three freeze-thaw cycles. The variability of selected samples for the incurred sample reanalysis was ≤12.7% when compared with the original sample concentrations. This validated LC-MS/MS method for determination of AGM-130 was used to support a phase 0 microdose clinical trial.

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Background Apelin is an endogenous neuropeptide that binds to the G-protein-coupled receptor (APJ) and participates in a variety of physiological processes in the heart, lungs, and other peripheral organs. Intriguingly, [Pyr1]-Apelin-13, a highly potent pyroglutamic form of apelin, has the potential to bind to and be degraded by angiotensin-converting enzyme 2 (ACE2). ACE2 is known to operate as a viral receptor in the early stages of severe acute respiratory coronavirus (SARS-CoV-2) infection. Aim This study aimed to determine if apelin protects against SARS-CoV-2 infection by inhibiting ACE2 binding to SARS-CoV-2 spike protein. Design and Methods To determine whether [Pyr1]-Apelin-13 inhibits ACE2 binding to the SARS-CoV-2 spike protein (S protein), we performed a cell-to-cell fusion assay using ACE2-expressing cells and S protein-expressing cells and a pseudovirus-based inhibition assay. We then analyzed publicly available transcriptome data while focusing on the beneficial effects of apelin on the lungs. Results We found that [Pyr1]-Apelin-13 inhibits cell-to-cell fusion mediated by ACE2 binding to the S protein. In this experiment, [Pyr1]-Apelin-13 protected human bronchial epithelial cells, infected with pseudo-typed lentivirus producing S protein, against viral infection. In the presence of [Pyr1]-Apelin-13, the level of viral spike protein expression was also reduced in a concentration-dependent manner. Transcriptome analysis revealed that apelin may control inflammatory responses to viral infection by inhibiting the nuclear factor kappa B pathway. Conclusion Apelin is a potential therapeutic candidate against SARS-CoV-2 infection.

Objectives: To compare meta-analysis results with and without adjustment for healthy worker effect on the association between working in the semiconductor industry and cancer mortality.Methods: A total of six studies that reported standardized mortality ratios (SMRs) for cancers were selected for meta-analysis. The SMR results from individual study were combined for all cancers and leukemia to estimate the summary SMRs and 95% confidence intervals (CIs) with random-effects model. To adjust for healthy worker effect, the relative SMRs (rSMR=SMRx/SMRnot x) were calculated using observed and expected counts for the specific cause of interest (i.e., all cancers and leukemia) and observed and expected counts for all the other causes of mortality. Then, the rSMR results were combined to estimate the summary rSMRs and 95% confidence intervals. Results:The SMRs for all causes among semiconductor industry workers ranged from 0.25 to 0.80, which reflects significant healthy worker effects. Remarkable difference was found between the summary SMRs and the summary rSMRs. The summary SMR for all cancers was 0.70 (95% CI=0.63-0.79) whereas the summary rSMR was 1.38 (1.20-1.59). The summary SMR for leukemia was 0.88 (0.72-1.07), and the summary rSMR was 1.88 (1.20-2.95). Conclusion:Our results suggest that the adjustment for the healthy worker effect (i.e., rSMR) may be useful in meta-analyses of cohort studies reporting SMRs.