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Antibody-dependent cellular cytotoxicity (ADCC) is exerted by immune cells expressing surface Fcγ receptors (FcγRs) against cells coated with antibody, such as virus-infected or transformed cells. CD16, the FcγRIIIA, is essential for ADCC by NK cells, and is also expressed by a subset of human blood monocytes. We found that human CD16− expressing monocytes have a broad spectrum of ADCC capacities and can kill cancer cell lines, primary leukemic cells and hepatitis B virus-infected cells in the presence of specific antibodies. Engagement of CD16 on monocytes by antibody bound to target cells activated β2-integrins and induced TNFα secretion. In turn, this induced TNFR expression on the target cells, making them susceptible to TNFα-mediated cell death. Treatment with TLR agonists, DAMPs or cytokines, such as IFNγ, further enhanced ADCC. Monocytes lacking CD16 did not exert ADCC but acquired this property after CD16 expression was induced by either cytokine stimulation or transient transfection. Notably, CD16+ monocytes from patients with leukemia also exerted potent ADCC. Hence, CD16+ monocytes are important effectors of ADCC, suggesting further developments of this property in the context of cellular therapies for cancer and infectious diseases.

The objective of this manuscript was to validate a physiologically-based pharmacokinetic (PBPK) model developed to characterize brain pharmacokinetics (PK) of monoclonal antibodies (mAbs) using novel large-pore microdialysis data generated in mice. To support this objective, brain, CSF, and ISF PK of a human anti-tetanus toxin (TeTx) antibody was measured in mice following intraperitoneal (IP) administration. This antibody has no binding in mice. In addition, our recently published mouse brain PK data generated following intravenous (IV) and IP administration of trastuzumab in mice, and other published PK data for brain disposition of antibody in mice, were used to evaluate the PBPK model. All the model parameters were obtained from literature or kept the same as in our previously published manuscript. The revised PBPK model was able to characterize the PK of antibodies in mice brain, CSF, and ISF reasonably well (i.e., within a three-fold error). However, a priori selected parameters led to underprediction of ISF PK during the initial phase of the profile. A local sensitivity analysis suggested that minor changes in several brain-related parameters can help overcome this discrepancy, where an increase in the convective flow of antibodies across BBB was found to be the most parsimonious way to capture all the PK profiles well. However, the presence of this pathway needs further validation. As such, here we have presented an improved PBPK model to characterize and predict the PK of mAbs in different regions of the mouse brain following systemic administration. This model can serve as a quantitative tool to facilitate the discovery, preclinical evaluation, and preclinical-to-clinical translation of novel antibodies targeted against CNS disorders.

Tumor-induced angiogenesis leads to the development of leaky tumor vessels devoid of structural and morphological integrity. Due to angiogenesis, elevated interstitial fluid pressure (IFP) and low blood perfusion emerge as common properties of the tumor microenvironment that act as barriers for drug delivery. In order to overcome these barriers, normalization of vasculature is considered to be a viable option. However, insight is needed into the phenomenon of normalization and in which conditions it can realize its promise. In order to explore the effect of microenvironmental conditions and drug scheduling on normalization benefit, we build a mathematical model that incorporates tumor growth, angiogenesis and IFP. We administer various theoretical combinations of antiangiogenic agents and cytotoxic nanoparticles through heterogeneous vasculature that displays a similar morphology to tumor vasculature. We observe differences in drug extravasation that depend on the scheduling of combined therapy; for concurrent therapy, total drug extravasation is increased but in adjuvant therapy, drugs can penetrate into deeper regions of tumor.

Pharmacological concentrations of small molecule natural products, such as ascorbic acid, have exhibited distinct cell killing outcomes between cancer and normal cells whereby cancer cells undergo apoptosis or necrosis while normal cells are not adversely affected. Here, we develop a mathematical model for ascorbic acid that can be utilized as a tool to understand the dynamics of reactive oxygen species (ROS) induced cell death. We determine that not only do endogenous antioxidants such as catalase contribute to ROS-induced cell death, but also cell membrane properties play a critical role in the efficacy of ROS as a cytotoxic mechanism against cancer cells vs. normal cells. Using in vitro assays with breast cancer cells, we have confirmed that cell membrane properties are essential for ROS, in the form of hydrogen peroxide (H 2 O 2 ), to induce cell death. Interestingly, we did not observe any correlation between intracellular H 2 O 2 and cell survival, suggesting that cell death by H 2 O 2 is triggered by interaction with the cell membrane and not necessarily due to intracellular levels of H 2 O 2 . These findings provide a putative mechanistic explanation for the efficacy and selectivity of therapies such as ascorbic acid that rely on ROS-induced cell death for their anti-tumor properties.

The role of central nervous system (CNS) prophylaxis with high-dose methotrexate (HDMTX) in DLBCL is controversial. In this retrospective study, we evaluated the efficacy of prophylactic HDMTX on isolated CNS relapse, concomitant CNS and systemic relapse, systemic relapse, and survival outcomes in 226 patients with newly diagnosed DLBCL and high-risk CNS International Prognostic Index (CNS-IPI) score treated with RCHOP. The three-year risk of isolated CNS relapse was significantly lower in patients who received HDMTX, at 3.1% compared to 14.6% (P = 0.032) in those who did not. However, neither concomitant CNS-systemic relapse rates, systemic relapse rates, nor three-year PFS and OS were significantly different between treatment groups in multivariable analysis. Among propensity score-matched patients (N = 102), HDMTX was also associated with significantly lower isolated CNS relapse rates (HR 0.06, 95% CI 0.004–0.946, P = 0.046). HDMTX was well tolerated with manageable toxicities when given at a dose of 3 g/m2 by day 3 of RCHOP chemotherapy. Using propensity score matching and multivariable regression to yield treatment groups with well-balanced covariates, we showed that prophylactic HDMTX improved isolated CNS relapse rates but did not decrease concomitant CNS-systemic relapse rates, systemic relapse rates, or improve survival outcomes.

Scientific Reports 6: Article number: 34310; published online 27 September 2016; updated: 07 April 2017. This Article contains errors in Figure 3, which was inadvertently split in two, resulting in the omission of Figure 7 and the mislabelling of Figures 4, 5 and 6. The correct Figures 3, 4, 5, 6 and 7 appear below as Figures 1, 2, 3, 4 and 5 respectively.

Antibody-drug conjugates (ADCs) are ushering in the next era of targeted therapy against cancer. An ADC for cancer therapy consists of a potent cytotoxic payload that is attached to a tumour-targeted antibody by a chemical linker, usually with an average drug-to-antibody ratio (DAR) of 3.5–4. The theory is to deliver potent cytotoxic payloads directly to tumour cells while sparing healthy cells. However, practical application has proven to be more difficult. At present there are only two ADCs approved for clinical use. Nevertheless, in the last decade there has been an explosion of options for ADC engineering to optimize target selection, Fc receptor interactions, linker, payload and more. Evaluation of these strategies requires an understanding of the mechanistic underpinnings of ADC pharmacokinetics. Development of ADCs for use in cancer further requires an understanding of tumour properties and kinetics within the tumour environment, and how the presence of cancer as a disease will impact distribution and elimination. Key pharmacokinetic considerations for the successful design and clinical application of ADCs in oncology are explored in this review, with a focus on the mechanistic determinants of distribution and elimination.

Extranodal natural killer T-cell lymphoma (NKTCL), nasal type, is a rare and aggressive malignancy that occurs predominantly in Asian and Latin American populations. Although Epstein-Barr virus infection is a known risk factor, other risk factors and the pathogenesis of NKTCL are not well understood. We aimed to identify common genetic variants affecting individual risk of NKTCL. We did a genome-wide association study of 189 patients with extranodal NKTCL, nasal type (WHO classification criteria; cases) and 957 controls from Guangdong province, southern China. We validated our findings in four independent case-control series, including 75 cases from Guangdong province and 296 controls from Hong Kong, 65 cases and 983 controls from Guangdong province, 125 cases and 1110 controls from Beijing (northern China), and 60 cases and 2476 controls from Singapore. We used imputation and conditional logistic regression analyses to fine-map the associations. We also did a meta-analysis of the replication series and of the entire dataset. Associations exceeding the genome-wide significance threshold (p<5 × 10−8) were seen at 51 single-nucleotide polymorphisms (SNPs) mapping to the class II MHC region on chromosome 6, with rs9277378 (located in HLA-DPB1) having the strongest association with NKTCL susceptibility (p=4·21 × 10−19, odds ratio [OR] 1·84 [95% CI 1·61–2·11] in meta-analysis of entire dataset). Imputation-based fine-mapping across the class II MHC region suggests that four aminoacid residues (Gly84-Gly85-Pro86-Met87) in near-complete linkage disequilibrium at the edge of the peptide-binding groove of HLA-DPB1 could account for most of the association between the rs9277378*A risk allele and NKTCL susceptibility (OR 2·38, p value for haplotype 2·32 × 10−14). This association is distinct from MHC associations with Epstein-Barr virus infection. To our knowledge, this is the first time that a genetic variant conferring an NKTCL risk is noted at genome-wide significance. This finding underlines the importance of HLA-DP antigen presentation in the pathogenesis of NKTCL. Top-Notch Young Talents Program of China, Special Support Program of Guangdong, Specialized Research Fund for the Doctoral Program of Higher Education (20110171120099), Program for New Century Excellent Talents in University (NCET-11-0529), National Medical Research Council of Singapore (TCR12DEC005), Tanoto Foundation Professorship in Medical Oncology, New Century Foundation Limited, Ling Foundation, Singapore National Cancer Centre Research Fund, and the US National Institutes of Health (1R01AR062886, 5U01GM092691-04, and 1R01AR063759-01A1).

In this work we proposed a population physiologically-based pharmacokinetic (popPBPK) framework for quantifying and predicting inter-individual pharmacokinetic variability using the anti-HER2 monoclonal antibody (mAb) trastuzumab as an example. First, a PBPK model was developed to account for the possible mechanistic sources of variability. Within the model, five key factors that contribute to variability were identified and the nature of their contribution was quantified with local and global sensitivity analyses. The five key factors were the concentration of membrane-bound HER2 ( A g ), the convective flow rate of mAb through vascular pores ( F 2 ), the endocytic transport rate of mAb through vascular endothelium ( C L u p ), the degradation rate of mAb-HER2 complexes ( K d e g A g ) and the concentration of shed HER2 extracellular domain in circulation ( E C D ). F 2 was the most important parameter governing trastuzumab distribution into tissues and primarily affected variability in the first 500 h post-administration. A g was the most significant contributor to variability in clearance. These findings were used together with population generation methods to accurately predict the observed variability in four experimental trials with trastuzumab. To explore anthropometric sources of variability, virtual populations were created to represent participants in the four experimental trials. Using populations with only their expected anthropometric diversity resulted in under-prediction of the observed inter-individual variability. Adapting the populations to include literature-based variability around the five key parameters enabled accurate predictions of the variability in the four trials. The successful application of this framework demonstrates the utility of popPBPK methods to understand the mechanistic underpinnings of pharmacokinetic variability.

A Correction to this paper has been published: https://doi.org/10.1038/s41375-021-01195-4