Large Molecule Specific Assay Operation: Recommendation for Best Practices and Harmonization from the Global Bioanalysis Consortium Harmonization Team

The L2 Global Harmonization Team on large molecule specific assay operation for protein bioanalysis in support of pharmacokinetics focused on the following topics: setting up a balanced validation design, specificity testing, selectivity testing, dilutional linearity, hook effect, parallelism, and testing of robustness and ruggedness. The team additionally considered the impact of lipemia, hemolysis, and the presence of endogenous analyte on selectivity assessments as well as the occurrence of hook effect in study samples when no hook effect had been observed during pre-study validation.     

Dapagliflozin and measures of cardiovascular autonomic function in patients with type 2 diabetes (T2D)

AimsSodium-glucose cotransporter-2 (SGLT-2) inhibitors reduce blood pressure without compensatory heart rate elevation, possibly by modulating sympathetic/parasympathetic activity. This may contribute to their cardiovascular benefits in type 2 diabetes (T2D). We evaluated the effects of dapagliflozin (DAPA) on measures of cardiovascular autonomic neuropathy (CAN), cardiac function, and glucose variability (GV) in T2D.MethodsPilot, randomized, two-period crossover trial comparing 12-week DAPA versus 12-week glimepiride treatment on CAN measures (cardiovascular autonomic reflex tests and heart rate variability), B-type natriuretic peptide (BNP), and GV (Abbott's Libre Pro devices) using signed rank tests and mixed models from baseline to 12 weeks within and between each period.ResultsForty-five T2D participants on metformin monotherapy (mean age 57 ± 8 years, duration 7 ± 6 years, HbA1c 7.8 ± 1.3%) were enrolled with 41 completing the trial. There were no differences in CAN indices or BNP with each drug compared to baseline and each other. Participants on DAPA demonstrated greater weight loss, reduced time in hypoglycemia, and improved GV compared to glimepiride.ConclusionsShort term treatment with DAPA did not affect CAN measures or BNP in uncomplicated and relatively healthy T2D participants. Longer prospective studies in patients with advanced disease are needed to better understand relationships between SGLT-2 inhibitors and CAN.Clinical trial registration: NCT02973477     

Caring for Women Experiencing Breast Engorgement: A Case Report

Breast engorgement is an uncomfortable and sometimes painful component of the postpartum period. The effective treatment of breast engorgement may provide an avenue for clinicians to improve postpartum care for women and promote breastfeeding. This case report presents one woman's experience with breast engorgement in the early postpartum period. The etiology, evidence‐based practices for treatment, clinical implications, and recommendations for practice are reviewed. The importance of interprofessional care to minimize conflicting information a lactating woman receives is highlighted. Interprofessional teamwork can optimize care to resolve breast engorgement and facilitate a woman achieving her breastfeeding goals.     

The Role of Noninvasive Endpoints in Predicting Long-Term Outcomes in Pulmonary Arterial Hypertension

Background

Until recently, many clinical trials in patients with pulmonary arterial hypertension (PAH) evaluated exercise capacity with 6-minute walk distance (6MWD) as the primary endpoint. Common secondary endpoints include PAH functional class (FC), which assesses symptoms, and either brain natriuretic peptide (BNP) or the inactive N-terminal cleavage product of its prohormone (NT-proBNP), which assesses cardiac function.

Objective

Examine the relationships among 6MWD, FC, and BNP/NT-proBNP measured at baseline or follow-up with long-term outcomes in PAH studies.

Methods

Relevant literature from January 1990 to April 2018 were obtained by searching PubMed, Embase, and Cochrane. Articles in English reporting on associations between 6MWD, FC, or BNP/NT-proBNP and outcomes in PAH were identified. Each endpoint was evaluated individually. Prespecified inclusion and exclusion criteria were applied at level 1 (titles/abstracts) and level 2 (full-text review).

Results

The database search yielded 836 unique records; 65 full-text articles were reviewed. Twenty-five studies were eligible for inclusion. Findings supported the importance of measuring PAH noninvasive endpoints in predicting long-term outcomes. Patients with shorter or decreased 6MWD, poor (III/IV) or declining FC (e.g., from II to III), or elevated or increasing BNP/NT-proBNP had a higher risk of death and costly events (e.g., hospitalization, lung transplant). FC also predicted health care resource utilization and costs. Collectively, these endpoints establish risk groups that predict likelihood of complications from PAH or death.

Conclusion

Assessment of 6MWD, FC, and BNP/NT-proBNP provides low-cost, efficient, and noninvasive means of predicting long-term health and economic outcomes in patients with PAH.

     

An Approach to Bioactivity Assessment for Critical Quality Attribute Identification Based on Antibody-Antigen Complex Structure

Identification of critical quality attributes (CQAs) is an important step for development of biopharmaceuticals with intended performance. An accurate CQA assessment is needed to ensure product quality and focusing on development efforts where control is needed. The assignment of criticality is based on safety and efficacy. Efficacy is related to PK and bioactivity. Here, we developed a novel approach based on antibody-antigen complex structure and modeling as a complementary method for bioactivity assessment. To validate this approach, common product related quality attributes and mutagenesis data from several IgGs were assessed using available antibody-antigen complex structures, and results were compared with experimental data from bioactivity or binding affinity measurements. A stepwise evaluation scheme for structural based analysis is proposed; based on systematic assessment following the scheme, good correlation has been observed between structural analysis and experimental data. This demonstrates that such an approach can be applied as a complementary tool for bioactivity assessment. Main applications are 1) To decouple multiple attributes to achieve amino acid resolution for bioactivity assessment, 2) To assess bioactivity of attributes that cannot be experimentally generated, 3) To provide molecular mechanism for experimental observation and understand structure function relationship. Examples are provided to illustrate these applications.     

Substance-abusing mothers and fathers' willingness to allow their children to receive mental health treatment

The purpose of this study was to examine attitudes of substance-abusing mothers and fathers entering outpatient treatment toward allowing their children to participate in individual- or family-based interventions. Data were collected from a brief anonymous survey completed by adults at intake into a large substance abuse treatment program in western New York. Only one-third of parents reported that they would be willing to allow their children to participate in any form of mental health treatment. Results of chi-square analyses revealed that a significantly greater proportion of mothers reported that they would allow their children to participate in mental health treatment (41%) compared to fathers (28%). Results of logistic regression analyses revealed even after controlling for child age, mothers were more likely than fathers to indicate their willingness to allow their children to receive mental health treatment; however, type of substance abuse (alcohol versus drug abuse) was not associated with parents' willingness to allow their children to receive treatment. Parental reluctance to allow their children to receive individual or family-based treatment is a significant barrier in efforts to intervene with these at-risk children.     

Nonparametric coalescent inference of mutation spectrum history and demography

As populations boom and bust, the accumulation of genetic diversity is modulated, encoding histories of living populations in present-day variation. Many methods exist to decode these histories, and all must make strong model assumptions. It is typical to assume that mutations accumulate uniformly across the genome at a constant rate that does not vary between closely related populations. However, recent work shows that mutational processes in human and great ape populations vary across genomic regions and evolve over time. This perturbs the mutation spectrum (relative mutation rates in different local nucleotide contexts). Here, we develop theoretical tools in the framework of Kingman’s coalescent to accommodate mutation spectrum dynamics. We present mutation spectrum history inference (mushi), a method to perform nonparametric inference of demographic and mutation spectrum histories from allele frequency data. We use mushi to reconstruct trajectories of effective population size and mutation spectrum divergence between human populations, identify mutation signatures and their dynamics in different human populations, and calibrate the timing of a previously reported mutational pulse in the ancestors of Europeans. We show that mutation spectrum histories can be placed in a well-studied theoretical setting and rigorously inferred from genomic variation data, like other features of evolutionary history.

Over the past decade, population geneticists have developed many sophisticated methods for inferring population demography and have consistently found that simple isolated populations of constant size are far from the norm (reviewed in refs. 1–3). Population expansions and founder events, as well as migration between species and geographic regions, have been inferred from virtually all high-resolution genetic datasets. We now recognize that inferring these nonequilibrium demographies is often essential for understanding the histories of adaptation and global migration. Population genetics has uncovered many features of human history that were once virtually unknowable by other means, revealing a complex series of migrations, population replacements, and admixture networks among human groups and extinct hominoids.Although demographic inference methods can model complex population histories, the germline mutation process that creates variation has long received a comparatively simple treatment. A single parameter, μ, is used to represent the mutation rate per generation at all loci, in all individuals, and at all times. In humans, μ is estimated from parent–child trio sequencing studies, and modest variation in μ can have major effects on the interpretation of inferred parameters, such as times of admixture and population divergence. In other organisms, for which trio sequence data are usually unavailable, μ is estimated from sequence divergence between species with a fossil-calibrated divergence time, and these estimates come with still higher uncertainty.A growing body of evidence indicates that simple, constant mutation rate models may not adequately describe how variation accumulates on either inter- or intraspecific timescales (4–7). Germline mutation rates appear to have evolved during the speciation of great apes and the divergence of modern human populations (reviewed in ref. 8). Much of this evolution might be caused by nearly neutral drift (9), but a contributing factor could be selection on traits, like life history and chromatin structure, that indirectly affect mutation accumulation. Because mutation is intimately tied to the basic housekeeping process of cell division, gamete production, and embryonic development, the accumulation of mutations is likely to be complexly coupled to other biological processes (10–12).It is difficult to disentangle past changes in mutation rate from past changes in effective population size, which modulate levels of polymorphism even when the mutation rate stays constant. However, evolution of the mutation process can be indirectly detected by measuring its effects on the mutation spectrum: the relative mutation rates among different local nucleotide contexts. Hwang and Green (13) modeled the triplet context dependence of the substitution process in a mammalian phylogeny, finding varying contributions from replication errors, cytosine deamination, and biased gene conversion and showing that the relative rates of these processes varied between different mammalian lineages. Many cancers also exhibit somatic hypermutability of certain triplet motifs due to different DNA damage agents and failure points in the DNA repair process (14, 15). Harris (6) and Harris and Pritchard (7) examined the variation of triplet spectra between closely related populations, counting single-nucleotide variants in each triplet mutation type as a proxy for mutational input. They found that human triplet spectra distinctly cluster by continental ancestry group and that historical pulses in mutation activity influence the distribution of allele frequencies in certain mutation types. The divergence of mutation spectra among human continental groups has been replicated in independently generated datasets (7, 16), and similar patterns have been observed in other species, including great apes (17), mice (18), and yeast (19). Some of the mutation spectrum divergence between mice and yeast lineages has been mapped to mutator alleles (19, 20).Emerging from the literature is a picture of a mutation process evolving within and between populations, anchored to genomic features and accented by spectra of local nucleotide context. If probabilistic models of population genetic processes are to keep pace with these empirical findings, mutation deserves a richer treatment in state-of-the-art inference tools. In this paper, we build on classical theoretical tools to introduce fast nonparametric inference of population-level mutation spectrum history (MuSH)—the relative mutation rate in different local nucleotide contexts across time—alongside inference of demographic history. Whereas previous work has uncovered mutation spectrum evolution using summary statistics of standing variation, we shift perspective to focus on inference of the MuSH, which we model on the same footing as demography.Demographic inference requires us to invert the map that takes population history to the patterns of genetic diversity observable today. This task is often simplified by first compressing these genetic diversity data into a summary statistic such as the sample frequency spectrum (SFS), the distribution of derived allele frequencies among sampled haplotypes. The SFS is a well-studied population genetic summary statistic that is sensitive to demographic history. Inverting the map from demographic history to SFS is a notoriously ill-posed problem, in that many different population histories can have identical expected SFS (21–25). One way to deal with the ill posedness of demographic inference is to specify a parametric model of population size change, usually piecewise linear or piecewise exponential. An alternative, which generalizes to other inverse problems, is to allow a more general space of solutions but to regularize by penalizing histories that contain biologically unrealistic features (e.g., high-frequency population size oscillations). Both approaches shrink the set of feasible solutions to the inverse problem so that it becomes well posed and can be thought of as leveraging prior knowledge. In particular, regularization constrains the population size from changing on arbitrarily small timescales since significant population size change usually takes at least a few generations.In this paper, we extend a coalescent framework for demographic inference to accommodate inference of the MuSH from an SFS that is resolved into different local k-mer nucleotide contexts. This is a richer summary statistic that we call the k-SFS where, for example, k=3 means triplet context. We show using coalescent theory that the k-SFS is related to the MuSH by a linear transformation while depending nonlinearly on the demographic history. We infer both demographic history and MuSH by optimizing a cost that balances a data-fitting term using the forward map from coalescent theory, along with regularization terms that favor solutions with low complexity. Our open-source software mushi (mutation spectrum history inference) is available in ref. 26 as a Python package with extensive documentation. Using default settings and modest hardware, mushi takes only a few seconds to infer histories from population-scale sample frequency data.The recovered MuSH is a rich object that illuminates dimensions of population history and addresses biological questions about the evolution of the mutation process. After validating with data simulated under known histories, we use mushi to independently infer histories for each of the 26 populations (from 5 superpopulations defined by continental ancestry) from the 1000 Genomes Project (1KG) Consortium (27) using recent high-coverage sequencing data (28). We demonstrate that mushi is a powerful tool for demographic inference that has several advantages over existing demographic inference methods and then go on to describe the illuminated features of human MuSH.We recover demographic features that are robust to regularization parameter choices, including the out-of-Africa event and the more recent bottleneck in the ancestors of modern Finns, and we find that effective population sizes converge ancestrally within each superpopulation, despite being inferred independently. Decomposing human MuSH into mutation signatures varying through time in each population, we see global divergence in the mutation process that impacts many mutation types and reflects population and superpopulation relatedness. Finally, we revisit the timing of a previously reported ancient pulse of elevated TCC → TTC mutation rate, active primarily in the ancestors of Europeans and absent in East Asians (6, 7, 29, 30). We find that the extent of the pulse into the ancient past is sensitive to the choice of demographic history model but that all demographic models that fit the k-SFS yield a pulse timing that is significantly older than previously thought, seemingly arising near the divergence time of East Asians and Europeans.With mushi, we can quickly reconstruct demographic history and MuSH without strong model specification requirements. This adds an approach to the toolbox for researchers interested only in demographic inference. For researchers studying the mutation spectrum, demographic history is necessary for time calibration of events in mutation history, so we expect that jointly modeling demography and MuSH will be important for studying mutational spectrum evolution in population genetics.