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ABSTRACTThe development of bispecific antibodies that bind at least two different targets relies on bringing together multiple binding domains with different binding properties and biophysical characteristics to produce a drug-like therapeutic. These building blocks play an important role in the overall quality of the molecule and can influence many important aspects from potency and specificity to stability and half-life. Single-domain antibodies, particularly camelid-derived variable heavy domain of heavy chain (VHH) antibodies, are becoming an increasingly popular choice for bispecific construction due to their single-domain modularity, favorable biophysical properties, and potential to work in multiple antibody formats. Here, we review the use of VHH domains as building blocks in the construction of multispecific antibodies and the challenges in creating optimized molecules. In addition to exploring traditional approaches to VHH development, we review the integration of machine learning techniques at various stages of the process. Specifically, the utilization of machine learning for structural prediction, lead identification, lead optimization, and humanization of VHH antibodies.

Background Combining an immune checkpoint inhibitor with a tumor vaccine may modulate the immune system to leverage complementary mechanisms of action that lead to sustained T-cell activation and a potent prolonged immunotherapeutic response in metastatic castration resistant prostate cancer (mCRPC).Methods Subjects with asymptomatic or minimally symptomatic mCRPC were randomly assigned in a 1:1 ratio to receive either atezolizumab followed by sipuleucel-T (Arm 1) or sipuleucel-T followed by atezolizumab (Arm 2). The primary endpoint was safety, while secondary endpoints included preliminary clinical activity such as objective tumor response and systemic immune responses that could identify key molecular and immunological changes associated with sequential administration of atezolizumab and sipuleucel-T.Results A total of 37 subjects were enrolled. The median age was 75.0 years, median prostate specific antigen (PSA) was 21.9 ng/mL, and subjects had a median number of three prior treatments. Most subjects (83.8%) had at least one treatment-related adverse event. There were no grade 4 or 5 toxicities attributed to either study drug. Immune-related adverse events and infusion reactions occurred in 13.5% of subjects, and all of which were grade 1 or 2. Of 23 subjects with Response Evaluation Criteria in Solid Tumors measurable disease, only one subject in Arm 2 had a partial response (PR) and four subjects overall had stable disease (SD) at 6 months reflecting an objective response rate of 4.3% and a disease control rate of 21.7%. T-cell receptor diversity was higher in subjects with a response, including SD. Immune response to three novel putative antigens (SIK3, KDM1A/LSD1, and PIK3R6) appeared to increase with treatment.Conclusions Overall, regardless of the order in which they were administered, the combination of atezolizumab with sipuleucel-T appears to be safe and well tolerated with a comparable safety profile to each agent administered as monotherapy. Correlative immune studies may suggest the combination to be beneficial; however, further studies are needed.Trial registration number NCT03024216.

Cell type deconvolution from bulk expression data rely on a reference expression matrix. Here, the authors introduce a basis matrix built using data from both healthy and diseased samples profiled on 42 platforms, reducing biases introduced by single-platform matrices built using healthy samples.

Abstract Gene Ontology (GO) enrichment analysis is ubiquitously used for interpreting high throughput molecular data and generating hypotheses about underlying biological phenomena of experiments. However, the two building blocks of this analysis — the ontology and the annotations — evolve rapidly. We used gene signatures derived from 104 disease analyses to systematically evaluate how enrichment analysis results were affected by evolution of the GO over a decade. We found low consistency between enrichment analyses results obtained with early and more recent GO versions. Furthermore, there continues to be a strong annotation bias in the GO annotations where 58% of the annotations are for 16% of the human genes. Our analysis suggests that GO evolution may have affected the interpretation and possibly reproducibility of experiments over time. Hence, researchers must exercise caution when interpreting GO enrichment analyses and should reexamine previous analyses with the most recent GO version.

The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways’ differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling.

To gauge the current commitment to scientific research in the United States of America (US), we compared federal research funding (FRF) with the US gross domestic product (GDP) and industry research spending during the past six decades. In order to address the recent globalization of scientific research, we also focused on four key indicators of research activities: research and development (R&D) funding, total science and engineering doctoral degrees, patents, and scientific publications. We compared these indicators across three major population and economic regions: the US, the European Union (EU) and the People's Republic of China (China) over the past decade. We discovered a number of interesting trends with direct relevance for science policy. The level of US FRF has varied between 0.2% and 0.6% of the GDP during the last six decades. Since the 1960s, the US FRF contribution has fallen from twice that of industrial research funding to roughly equal. Also, in the last two decades, the portion of the US government R&D spending devoted to research has increased. Although well below the US and the EU in overall funding, the current growth rate for R&D funding in China greatly exceeds that of both. Finally, the EU currently produces more science and engineering doctoral graduates and scientific publications than the US in absolute terms, but not per capita. This study's aim is to facilitate a serious discussion of key questions by the research community and federal policy makers. In particular, our results raise two questions with respect to: a) the increasing globalization of science: "What role is the US playing now, and what role will it play in the future of international science?"; and b) the ability to produce beneficial innovations for society: "How will the US continue to foster its strengths?"