dPQL: a lossless distributed algorithm for generalized linear mixed model with application to privacy-preserving hospital profiling

ObjectiveTo develop a lossless distributed algorithm for generalized linear mixed model (GLMM) with application to privacy-preserving hospital profiling.Materials and MethodsThe GLMM is often fitted to implement hospital profiling, using clinical or administrative claims data. Due to individual patient data (IPD) privacy regulations and the computational complexity of GLMM, a distributed algorithm for hospital profiling is needed. We develop a novel distributed penalized quasi-likelihood (dPQL) algorithm to fit GLMM when only aggregated data, rather than IPD, can be shared across hospitals. We also show that the standardized mortality rates, which are often reported as the results of hospital profiling, can also be calculated distributively without sharing IPD. We demonstrate the applicability of the proposed dPQL algorithm by ranking 929 hospitals for coronavirus disease 2019 (COVID-19) mortality or referral to hospice that have been previously studied.ResultsThe proposed dPQL algorithm is mathematically proven to be lossless, that is, it obtains identical results as if IPD were pooled from all hospitals. In the example of hospital profiling regarding COVID-19 mortality, the dPQL algorithm reached convergence with only 5 iterations, and the estimation of fixed effects, random effects, and mortality rates were identical to that of the PQL from pooled data.ConclusionThe dPQL algorithm is lossless, privacy-preserving and fast-converging for fitting GLMM. It provides an extremely suitable and convenient distributed approach for hospital profiling.     

Characterization of the in vitro activity of AZD3409, a novel prenyl transferase inhibitor

Purpose

AZD3409 is a novel DPTI that has potent activity against both FTase and GGTase-1. The in vitro inhibition profile of AZD3409 was characterized using three different cell lines: mouse embryogenic fibroblasts, transfected with H-Ras^V12 (MEF), A549 cells (Ki4B-Ras mutation) and MCF-7 cells (no Ras mutation).

Methods

Both cytotoxicity and levels of inhibition of farnesylation and geranylgeranylation were determined in different assays in relation to the concentration of AZD3409. Results were compared with those obtained with the first-generation FTase inhibitor lonafarnib or the GGTase-1 inhibitor GGTI-2147.

Results

The mean IC₅₀ for cytotoxicity of AZD3409 and lonafarnib was 510 and 15,200?nM in MEF cells, 10,600 and 2,740?nM in A549 cells and 6,170 and 9,490?nM in MCF7 cells, respectively. In these cells, the IC₅₀ for FTase activity of AZD3409 ranged from 3.0 to 14.2?nM and of lonafarnib from 0.26 to 31.3?nM. The inhibiting activity of AZD3409 and lonafarnib on general protein farnesylation was comparable with the specific farnesylation levels of HDJ-2. In vitro geranylgeranylation of Rap1a could be inhibited by GGTI-2147 in all three cell lines, but only in MCF-7 cells by AZD3409. These results are in agreement with the IC₅₀ values for GGTase-1 activity as the lowest IC₅₀ for AZD3409 was found in the MCF-7 cell line.

Conclusions

AZD3409 inhibits farnesylation to a higher extent than geranylgeranylation. Both inhibition of farnesylation and geranylgeranylation could not be correlated to the antiproliferative activity of the drug.     

Macrocephaly-cutis marmorata telangiectatica congenita: a report on the natural history of a mild case

Macrocephaly-cutis marmorata telangiectatica congenita was first identified as a distinct syndrome in 1997. Since then there have been more than 10 further reports of the condition, several also comprising reviews of the earlier literature. Virtually all reported patients, however, are young children, and there is very little information about the natural evolution of the condition in adolescence and later life. This report describes a patient with features of macrocephaly-cutis marmorata telangiectatica congenita, though mildly affected, and her progression into teenage life. Her mild problems, many of which have largely resolved, demonstrate a possible more optimistic view of the condition than is currently held. The difficulties in making the diagnosis in an adult patient are also discussed.     

A predictive microfluidic model of human glioblastoma to assess trafficking of blood–brain barrier-penetrant nanoparticles

The blood–brain barrier represents a significant challenge for the treatment of high-grade gliomas, and our understanding of drug transport across this critical biointerface remains limited. To advance preclinical therapeutic development for gliomas, there is an urgent need for predictive in vitro models with realistic blood–brain-barrier vasculature. Here, we report a vascularized human glioblastoma multiforme (GBM) model in a microfluidic device that accurately recapitulates brain tumor vasculature with self-assembled endothelial cells, astrocytes, and pericytes to investigate the transport of targeted nanotherapeutics across the blood–brain barrier and into GBM cells. Using modular layer-by-layer assembly, we functionalized the surface of nanoparticles with GBM-targeting motifs to improve trafficking to tumors. We directly compared nanoparticle transport in our in vitro platform with transport across mouse brain capillaries using intravital imaging, validating the ability of the platform to model in vivo blood–brain-barrier transport. We investigated the therapeutic potential of functionalized nanoparticles by encapsulating cisplatin and showed improved efficacy of these GBM-targeted nanoparticles both in vitro and in an in vivo orthotopic xenograft model. Our vascularized GBM model represents a significant biomaterials advance, enabling in-depth investigation of brain tumor vasculature and accelerating the development of targeted nanotherapeutics.

High-grade gliomas are the most common primary malignant brain tumors in adults (1). These include grade IV astrocytomas, commonly known as glioblastoma multiforme (GBM), which account for more than 50% of all primary brain cancers and have dismal prognoses, with a 5-y survival rate of less than 5% (2). Due to their infiltrative growth into the healthy brain tissue, surgery often fails to eradicate all tumor cells (3). While chemotherapy and radiation modestly improve median survival (4), most patients ultimately succumb to their tumors. This is primarily due to the presence of a highly selective and regulated endothelium between blood and brain parenchyma known as the blood–brain barrier (BBB) (5), which limits the entry of therapeutics into the brain tissue where tumors are located. The BBB, characterized by a unique cellular architecture of endothelial cells (ECs), pericytes (PCs), and astrocytes (ACs) (6, 7), displays up-regulated expression of junctional proteins and reduced paracellular and transcellular transports compared to other endothelia (8). While this barrier protects the brain from toxins and pathogens, it also severely restricts the transport of many therapeutics, as evidenced by the low cerebrospinal fluid (CSF)-to-plasma ratio of most chemotherapeutic agents (9). There is thus an important need to develop new delivery strategies to cross the BBB and target tumors, enabling sufficient drug exposure (10).Despite rigorous research efforts to develop effective therapies for high-grade gliomas, the majority of trialed therapeutics have failed to improve outcomes in the clinic, even though the agents in question are effective against tumor cells in preclinical models (11). This highlights the inability of current preclinical models to accurately predict the performance of therapeutics in human patients. To address these limitations, we developed an in vitro microfluidic model of vascularized GBM tumors embedded in a realistic human BBB vasculature. This BBB-GBM platform features brain microvascular networks (MVNs) in close contact with a GBM spheroid, recapitulating the infiltrative properties of gliomas observed in the clinic (12) and those of the brain tumor vasculature, with low permeability, small vessel diameter, and increased expression of relevant junctional and receptor proteins (7). This platform is well suited for quantifying vascular permeability of therapeutics and simultaneously investigating modes of transport across the BBB and into GBM tumor cells.There is strong rationale for developing therapeutic nanoparticles (NPs) for GBM and other brain tumors, as they can be used to deliver a diverse range of therapeutic agents and, with appropriate functionalization, can be designed to exploit active transport mechanisms across the BBB (13, 14). Liposomal NPs have been employed in the oncology clinic to improve drug half-life and decrease systemic toxicity (15), but, to date, no nanomedicines have been approved for therapeutic indications in brain tumors. We hypothesize that a realistic BBB-GBM model composed entirely of human cells can accelerate preclinical development of therapeutic NPs. Using our BBB-GBM model, we investigated the trafficking of layer-by-layer NPs (LbL-NPs) and ultimately designed a GBM-targeted NP. The LbL approach leverages electrostatic assembly to generate modular NP libraries with highly controlled architecture. We have used LbL-NPs to deliver a range of therapeutic cargos in preclinical tumor models (16, 17) and have recently demonstrated that liposomes functionalized with BBB-penetrating ligands improved drug delivery across the BBB to GBM tumors (18). Consistent with clinical data (19), we observed that the low-density lipoprotein receptor-related protein 1 (LRP1) was up-regulated in the vasculature near GBM spheroids in the BBB-GBM model and leveraged this information to design and iteratively test a library of NPs. We show that the incorporation of angiopep-2 (AP2) peptide moieties on the surface of LbL-NPs leads to increased BBB permeability near GBM tumors through LRP1-mediated transcytosis. With intravital imaging, we compared the vascular permeabilities of dextran and LbL-NPs in the BBB-GBM platform to those in mouse brain capillaries and validated the predictive potential of our in vitro model. Finally, we show the capability of the BBB-GBM platform to screen therapeutic NPs and predict in vivo efficacy, demonstrating improved efficacy of cisplatin (CDDP) when encapsulated in GBM-targeting LbL-NPs both in vitro and in vivo.     

Gaps and opportunities in the treatment of relapsed-refractory multiple myeloma: Consensus recommendations of the NCI Multiple Myeloma Steering Committee

A wide variety of new therapeutic options for Multiple Myeloma (MM) have recently become available, extending progression-free and overall survival for patients in meaningful ways. However, these treatments are not curative, and patients eventually relapse, necessitating decisions on the appropriate choice of treatment(s) for the next phase of the disease. Additionally, an important subset of MM patients will prove to be refractory to the majority of the available treatments, requiring selection of effective therapies from the remaining options. Immunomodulatory agents (IMiDs), proteasome inhibitors, monoclonal antibodies, and alkylating agents are the major classes of MM therapies, with several options in each class. Patients who are refractory to one agent in a class may be responsive to a related compound or to a drug from a different class. However, rules for selection of alternative treatments in these situations are somewhat empirical and later phase clinical trials to inform those choices are ongoing. To address these issues the NCI Multiple Myeloma Steering Committee formed a relapsed/refractory working group to review optimal treatment choices, timing, and sequencing and provide recommendations. Additional issues considered include the role of salvage autologous stem cell transplantation, risk stratification, targeted approaches for genetic subsets of MM, appropriate clinical trial endpoints, and promising investigational agents. This report summarizes the deliberations of the working group and suggests potential avenues of research to improve the precision, timing, and durability of treatments for Myeloma.Subject terms: Combination drug therapy, Cancer therapeutic resistance, Targeted therapies     

Refeeding Syndrome: A Critical Reality in Patients with Chronic Disease

Malnutrition is one of the most frequent metabolic challenges in the population of chronically ill patients. This results in increased administration of nutritional therapy in inpatient settings, which poses the risk of side effects, in particular, the development of refeeding syndrome. If not managed accordingly, it leads to a significant rise in morbidity and mortality. However, despite its importance, evidence-based recommendations on the management of refeeding syndrome are largely lacking, and only a few randomized controlled trials have been conducted. In light of this, the aim of this review is to raise awareness of refeeding syndrome in chronically ill patients by critically reviewing recent literature and providing a short overview as well as diagnosis and treatment algorithms of this underreported metabolic condition. In summary, recent findings suggest undergoing risk assessment and stratification for every patient receiving nutritional therapy. According to this, adaptation of energy and fluid support during the replenishment phase should be implemented in the nutritional therapy for patients at high risk. Additionally, continuous monitoring should take place, and appropriate actions should be initiated when necessary.     

Global,regional and national burden of disease attributable to 19 selected occupational risk factors for 183 countries, 2000–2016: A systematic analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury

ObjectivesWe provide a brief introduction to the objectives, data, methods and results of the World Health Organization (WHO)/International Labor Organization (ILO) Joint Estimates of the Work-related Burden of Disease and Injury (WHO/ILO Joint Estimates), which estimated the burden attributable to 19 selected occupational risk factors.MethodsThe WHO/ILO Joint Estimates were produced within the global Comparative Risk Assessment framework, which attributes the burden of one specific health outcome (ie, disease/injury) to a specific occupational risk factor. For 39 established occupational risk factor-health outcome pairs, estimates are produced using population attributable fractions (PAF) from recent burden of disease estimates. For two additional pairs, PAF are calculated from new databases of exposure and risk ratios produced in WHO/ILO systematic reviews. Attributable disease burdens were estimated by applying the PAF to total disease burdens.ResultsGlobally in 2016, it is estimated that 1.88 [95% uncertainty range (UR) 1.84–1.92] million deaths and 89.72 (95% UR 88.61–90.83) million disability-adjusted life years were attributable to the 19 selected occupational risk factors and their health outcomes. A disproportionately large work-related burden of disease is observed in the WHO African Region (for disability-adjusted life years), South-East Asia Region, and Western Pacific Region (for deaths), males and older age groups.ConclusionsThe WHO/ILO Joint Estimates can be used for global monitoring of exposure to occupational risk factors and work-related burden of disease and to identify, plan, cost, implement and evaluate policies, programs and actions to prevent exposure to occupational risk factors and their associated burden.