Discovery of potent BET bromodomain 1 stereoselective inhibitors using DNA-encoded chemical library selections.

BRDT, BRD2, BRD3, and BRD4 comprise the bromodomain and extraterminal (BET) subfamily which contain two similar tandem bromodomains (BD1 and BD2). Selective BD1 inhibition phenocopies effects of tandem BET BD inhibition both in cancer models and, as we and others have reported of BRDT, in the testes. To find novel BET BD1 binders, we screened >4.5 billion molecules from our DNA-encoded chemical libraries with BRDT-BD1 or BRDT-BD2 proteins in parallel. A compound series enriched only by BRDT-BD1 was resynthesized off-DNA, uncovering a potent chiral compound, CDD-724, with >2,000-fold selectivity for inhibiting BRDT-BD1 over BRDT-BD2. CDD-724 stereoisomers exhibited remarkable differences in inhibiting BRDT-BD1, with the R-enantiomer (CDD-787) being 50-fold more potent than the S-enantiomer (CDD-786). From structure­activity relationship studies, we produced CDD-956, which maintained picomolar BET BD1 binding potency and high selectivity over BET BD2 proteins and had improved stability in human liver microsomes over CDD-787. BROMOscan profiling confirmed the excellent pan-BET BD1 affinity and selectivity of CDD-787 and CDD-956 on BD1 versus BD2 and all other BD-containing proteins. A cocrystal structure of BRDT-BD1 bound with CDD-956 was determined at 1.82 Å and revealed BRDT-BD1­specific contacts with the αZ and αC helices that explain the high affinity and selectivity for BET BD1 versus BD2. CDD-787 and CDD-956 maintain cellular BD1-selectivity in NanoBRET assays and show potent antileukemic activity in acute myeloid leukemia cell lines. These BET BD1-specific and highly potent compounds are structurally unique and provide insight into the importance of chirality to achieve BET specificity.

Discovery and characterization of bromodomain 2-specific inhibitors of BRDT.

Bromodomain testis (BRDT), a member of the bromodomain and extraterminal (BET) subfamily that includes the cancer targets BRD2, BRD3, and BRD4, is a validated contraceptive target. All BET subfamily members have two tandem bromodomains (BD1 and BD2). Knockout mice lacking BRDT-BD1 or both bromodomains are infertile. Treatment of mice with JQ1, a BET BD1/BD2 nonselective inhibitor with the highest affinity for BRD4, disrupts spermatogenesis and reduces sperm number and motility. To assess the contribution of each BRDT bromodomain, we screened our collection of DNA-encoded chemical libraries for BRDT-BD1 and BRDT-BD2 binders. High-enrichment hits were identified and resynthesized off-DNA and examined for their ability to compete with JQ1 in BRDT and BRD4 bromodomain AlphaScreen assays. These studies identified CDD-1102 as a selective BRDT-BD2 inhibitor with low nanomolar potency and >1,000-fold selectivity over BRDT-BD1. Structure-activity relationship studies of CDD-1102 produced a series of additional BRDT-BD2/BRD4-BD2 selective inhibitors, including CDD-1302, a truncated analog of CDD-1102 with similar activity, and CDD-1349, an analog with sixfold selectivity for BRDT-BD2 versus BRD4-BD2. BROMOscan bromodomain profiling confirmed the great affinity and selectivity of CDD-1102 and CDD-1302 on all BET BD2 versus BD1 with the highest affinity for BRDT-BD2. Cocrystals of BRDT-BD2 with CDD-1102 and CDD-1302 were determined at 2.27 and 1.90 Å resolution, respectively, and revealed BRDT-BD2 specific contacts that explain the high affinity and selectivity of these compounds. These BD2-specific compounds and their binding to BRDT-BD2 are unique compared with recent reports and enable further evaluation of their nonhormonal contraceptive potential in vitro and in vivo.

DNA-encoded chemistry technology yields expedient access to SARS-CoV-2 Mpro inhibitors.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has killed more than 4 million humans globally, but there is no bona fide Food and Drug Administration-approved drug-like molecule to impede the COVID-19 pandemic. The sluggish pace of traditional therapeutic discovery is poorly suited to producing targeted treatments against rapidly evolving viruses. Here, we used an affinity-based screen of 4 billion DNA-encoded molecules en masse to identify a potent class of virus-specific inhibitors of the SARS-CoV-2 main protease (Mpro) without extensive and time-consuming medicinal chemistry. CDD-1714, the initial three-building-block screening hit (molecular weight [MW] = 542.5 g/mol), was a potent inhibitor (inhibition constant [Ki] = 20 nM). CDD-1713, a smaller two-building-block analog (MW = 353.3 g/mol) of CDD-1714, is a reversible covalent inhibitor of Mpro (Ki = 45 nM) that binds in the protease pocket, has specificity over human proteases, and shows in vitro efficacy in a SARS-CoV-2 infectivity model. Subsequently, key regions of CDD-1713 that were necessary for inhibitory activity were identified and a potent (Ki = 37 nM), smaller (MW = 323.4 g/mol), and metabolically more stable analog (CDD-1976) was generated. Thus, screening of DNA-encoded chemical libraries can accelerate the discovery of efficacious drug-like inhibitors of emerging viral disease targets.

A Guide to Selecting Flexible Survival Models to Inform Economic Evaluations of Cancer Immunotherapies.

OBJECTIVES: Parametric models are routinely used to estimate the benefit of cancer drugs beyond trial follow-up. The advent of immune checkpoint inhibitors has challenged this paradigm, and emerging evidence suggests that more flexible survival models, which can better capture the shapes of complex hazard functions, might be needed for these interventions. Nevertheless, there is a need for an algorithm to help analysts decide whether flexible models are required and, if so, which should be chosen for testing. This position article has been produced to bridge this gap. METHODS: A virtual advisory board comprising 7 international experts with in-depth knowledge of survival analysis and health technology assessment was held in summer 2021. The experts discussed 24 questions across 6 topics: the current survival model selection procedure, data maturity, heterogeneity of treatment effect, cure and mortality, external evidence, and additions to existing guidelines. Their responses culminated in an algorithm to inform selection of flexible survival models. RESULTS: The algorithm consists of 8 steps and 4 questions. Key elements include the systematic identification of relevant external data, using clinical expert input at multiple points in the selection process, considering the future and the observed hazard functions, assessing the potential for long-term survivorship, and presenting results from all plausible models. CONCLUSIONS: This algorithm provides a systematic, evidence-based approach to justify the selection of survival extrapolation models for cancer immunotherapies. If followed, it should reduce the risk of selecting inappropriate models, partially addressing a key area of uncertainty in the economic evaluation of these agents.

Metabolism of a Selective Serotonin and Norepinephrine Reuptake Inhibitor Duloxetine in Liver Microsomes and Mice.

Duloxetine (DLX) is a dual serotonin and norepinephrine reuptake inhibitor, widely used for the treatment of major depressive disorder. Although DLX has shown good efficacy and safety, serious adverse effects (e.g., liver injury) have been reported. The mechanisms associated with DLX-induced toxicity remain elusive. Drug metabolism plays critical roles in drug safety and efficacy. However, the metabolic profile of DLX in mice is not available, although mice serve as commonly used animal models for mechanistic studies of drug-induced adverse effects. Our study revealed 39 DLX metabolites in human/mouse liver microsomes and mice. Of note, 13 metabolites are novel, including five N-acetyl cysteine adducts and one reduced glutathione (GSH) adduct associated with DLX. Additionally, the species differences of certain metabolites were observed between human and mouse liver microsomes. CYP1A2 and CYP2D6 are primary enzymes responsible for the formation of DLX metabolites in liver microsomes, including DLX-GSH adducts. In summary, a total of 39 DLX metabolites were identified, and species differences were noticed in vitro. The roles of CYP450s in DLX metabolite formation were also verified using human recombinant cytochrome P450 (P450) enzymes and corresponding chemical inhibitors. Further studies are warranted to address the exact role of DLX metabolism in its adverse effects in vitro (e.g., human primary hepatocytes) and in vivo (e.g., Cyp1a2-null mice). SIGNIFICANCE STATEMENT: This current study systematically investigated Duloxetine (DLX) metabolism and bioactivation in liver microsomes and mice. This study provided a global view of DLX metabolism and bioactivation in liver microsomes and mice, which are very valuable to further elucidate the mechanistic study of DLX-related adverse effects and drug-drug interaction from metabolic aspects.

Estimating and Extrapolating Survival Using a State-Transition Modeling Approach: A Practical Application in Multiple Myeloma.

OBJECTIVES: State-transition models (STMs) applied in oncology have given limited considerations to modeling postprogression survival data. This study presents an application of an STM focusing on methods to evaluate the postprogression transition and its impact on survival predictions. METHODS: Data from the lenalidomide plus dexamethasone arm of the ASPIRE trial was used to estimate transition rates for an STM. The model accounted for the competing risk between the progression and preprogression death events and included an explicit structural link between the time to progression and subsequent death. The modeled transition rates were used to simulate individual disease trajectories in a discrete event simulation framework, based on which progression-free survival and overall survival over a 30-year time horizon were estimated. Survival predictions were compared with the observed trial data, matched external data, and estimates obtained from a more conventional partitioned survival analysis approach. RESULTS: The rates of progression and preprogression death were modeled using piecewise exponential functions. The rate of postprogression mortality was modeled using an exponential function accounting for the nonlinear effect of the time to progression. The STM provided survival estimates that closely fitted the trial data and gave more plausible long-term survival predictions than the best-fitting Weibull model applied in a partitioned survival analysis. CONCLUSIONS: The fit of the STM suggested that the modeled transition rates accurately captured the underlying disease process over the modeled time horizon. The considerations of this study may apply to other settings and facilitate a wider use of STMs in oncology.

Syntaxins 6 and 8 facilitate tau into secretory pathways.

Tau pathology initiates in defined brain regions and is known to spread along neuronal connections as symptoms progress in Alzheimer's disease (AD) and other tauopathies. This spread requires the release of tau from donor cells, but the underlying molecular mechanisms remained unknown. Here, we established the interactome of the C-terminal tail region of tau and identified syntaxin 8 (STX8) as a mediator of tau release from cells. Similarly, we showed the syntaxin 6 (STX6), part of the same SNARE family as STX8 also facilitated tau release. STX6 was previously genetically linked to progressive supranuclear palsy (PSP), a tauopathy. Finally, we demonstrated that the transmembrane domain of STX6 is required and sufficient to mediate tau secretion. The differential role of STX6 and STX8 in alternative secretory pathways suggests the association of tau with different secretory processes. Taken together, both syntaxins, STX6 and STX8, may contribute to AD and PSP pathogenesis by mediating release of tau from cells and facilitating pathology spreading.

Reducing persecution is more effective for restoring large carnivores than restoring their prey.

Large carnivores are currently disappearing from many world regions because of habitat loss, prey depletion, and persecution. Ensuring large carnivore persistence requires safeguarding and sometimes facilitating the expansion of their populations. Understanding which conservation strategies, such as reducing persecution or restoring prey, are most effective to help carnivores to reclaim their former ranges is therefore important. Here, we systematically explored such alternative strategies for the endangered Persian leopard (Panthera pardus saxicolor) in the Caucasus. We combined a rule-based habitat suitability map and a spatially explicit leopard population model to identify potential leopard subpopulations (i.e., breeding patches), and to test the effect of different levels of persecution reduction and prey restoration on leopard population viability across the entire Caucasus ecoregion and northern Iran (about 737,000 km2 ). We identified substantial areas of potentially suitable leopard habitat (~120,000 km2 ), most of which is currently unoccupied. Our model revealed that leopards could potentially recolonize these patches and increase to a population of >1,000 individuals in 100 yr, but only in scenarios of medium to high persecution reduction and prey restoration. Overall, reducing persecution had a more pronounced effect on leopard metapopulation viability than prey restoration: Without conservation strategies to reduce persecution, leopards went extinct from the Caucasus in all scenarios tested. Our study highlights the importance of persecution reduction in small populations, which should hence be prioritized when resources for conservation are limited. We show how individual-based, spatially explicit metapopulation models can help in quantifying the recolonization potential of large carnivores in unoccupied habitat, designing adequate conservation strategies to foster such recolonizations, and anticipating the long-term prospects of carnivore populations under alternative scenarios. Our study also outlines how data scarcity, which is typical for threatened range-expanding species, can be overcome with a rule-based habitat map. For Persian leopards, our projections clearly suggest that there is a large potential for a viable metapopulation in the Caucasus, but only if major conservation actions are taken towards reducing persecution and restoring prey.

Classifying information-sharing methods.

BACKGROUND: Sparse relative effectiveness evidence is a frequent problem in Health Technology Assessment (HTA). Where evidence directly pertaining to the decision problem is sparse, it may be feasible to expand the evidence-base to include studies that relate to the decision problem only indirectly: for instance, when there is no evidence on a comparator, evidence on other treatments of the same molecular class could be used; similarly, a decision on children may borrow-strength from evidence on adults. Usually, in HTA, such indirect evidence is either included by ignoring any differences ('lumping') or not included at all ('splitting'). However, a range of more sophisticated methods exists, primarily in the biostatistics literature. The objective of this study is to identify and classify the breadth of the available information-sharing methods. METHODS: Forwards and backwards citation-mining techniques were used on a set of seminal papers on the topic of information-sharing. Papers were included if they specified (network) meta-analytic methods for combining information from distinct populations, interventions, outcomes or study-designs. RESULTS: Overall, 89 papers were included. A plethora of evidence synthesis methods have been used for information-sharing. Most papers (n=79) described methods that shared information on relative treatment effects. Amongst these, there was a strong emphasis on methods for information-sharing across multiple outcomes (n=42) and treatments (n=25), with fewer papers focusing on study-designs (n=23) or populations (n=8). We categorise and discuss the methods under four 'core' relationships of information-sharing: functional, exchangeability-based, prior-based and multivariate relationships, and explain the assumptions made within each of these core approaches. CONCLUSIONS: This study highlights the range of information-sharing methods available. These methods often impose more moderate assumptions than lumping or splitting. Hence, the degree of information-sharing that they impose could potentially be considered more appropriate. Our identification of four 'core' methods of information-sharing allows for an improved understanding of the assumptions underpinning the different methods. Further research is required to understand how the methods differ in terms of the strength of sharing they impose and the implications of this for health care decisions.

Pharmacological Characterization of Low Molecular Weight Biased Agonists at the Follicle Stimulating Hormone Receptor.

Follicle-stimulating hormone receptor (FSHR) plays a key role in reproduction through the activation of multiple signaling pathways. Low molecular weight (LMW) ligands composed of biased agonist properties are highly valuable tools to decipher complex signaling mechanisms as they allow selective activation of discrete signaling cascades. However, available LMW FSHR ligands have not been fully characterized yet. In this context, we explored the pharmacological diversity of three benzamide and two thiazolidinone derivatives compared to FSH. Concentration/activity curves were generated for Gαs, Gαq, Gαi, ß-arrestin 2 recruitment, and cAMP production, using BRET assays in living cells. ERK phosphorylation was analyzed by Western blotting, and CRE-dependent transcription was assessed using a luciferase reporter assay. All assays were done in either wild-type, Gαs or ß-arrestin 1/2 CRISPR knockout HEK293 cells. Bias factors were calculated for each pair of read-outs by using the operational model. Our results show that each ligand presented a discrete pharmacological efficacy compared to FSH, ranging from super-agonist for ß-arrestin 2 recruitment to pure Gαs bias. Interestingly, LMW ligands generated kinetic profiles distinct from FSH (i.e., faster, slower or transient, depending on the ligand) and correlated with CRE-dependent transcription. In addition, clear system biases were observed in cells depleted of either Gαs or ß-arrestin genes. Such LMW properties are useful pharmacological tools to better dissect the multiple signaling pathways activated by FSHR and assess their relative contributions at the cellular and physio-pathological levels.

Partitioned Survival and State Transition Models for Healthcare Decision Making in Oncology: Where Are We Now?

OBJECTIVES: Partitioned survival models (PSMs) are routinely used to inform reimbursement decisions for oncology drugs. We discuss the appropriateness of PSMs compared to the most common alternative, state transition models (STMs). METHODS: In 2017, we published a National Institute for Health and Care Excellence (NICE) Technical Support Document (TSD 19) describing and critically reviewing PSMs. This article summarizes findings from TSD 19, reviews new evidence comparing PSMs and STMs, and reviews recent NICE appraisals to understand current practice. RESULTS: PSMs evaluate state membership differently from STMs and do not include a structural link between intermediate clinical endpoints (eg, disease progression) and survival. PSMs directly consider clinical trial endpoints and can be developed without access to individual patient data, but limit the scope for sensitivity analyses to explore clinical uncertainties in the extrapolation period. STMs facilitate these sensitivity analyses but require development of robust survival models for individual health-state transitions. Recent work has shown PSMs and STMs can produce substantively different survival extrapolations and that extrapolations from STMs are heavily influenced by specification of the underlying survival models. Recent NICE appraisals have not generally included both model types, reviewed individual clinical event data, or scrutinized life-years accrued in individual health states. CONCLUSIONS: The credibility of survival predictions from PSMs and STMs, including life-years accrued in individual health states, should be assessed using trial data on individual clinical events, external data, and expert opinion. STMs should be used alongside PSMs to support assessment of clinical uncertainties in the extrapolation period, such as uncertainty in post-progression survival.

Partnering With Patients to Bridge Gaps in Consent for Acute Care Research.

Clinical trials for acute conditions such as myocardial infarction and stroke pose challenges related to informed consent due to time limitations, stress, and severe illness. Consent processes should be sensitive to the context in which trials are conducted and to needs of patients and surrogate decision-makers. This manuscript describes a collaborative effort between ethicists, researchers, patients, and surrogates to develop patient-driven, patient-centered approaches to consent for clinical trials in acute myocardial infarction and stroke.Our group identified important ways in which existing consent processes and forms for clinical trials fail to meet patients' and surrogates' needs in the acute context. We collaborated to create model forms and consent processes that are substantially shorter and, hopefully, better-matched to patients' and surrogates' needs and expectations from the perspective of content, structure, and tone. These changes, however, challenge some common conventions regarding consent.

Migraine day frequency in migraine prevention: longitudinal modelling approaches.

BACKGROUND: Health economic models are critical tools to inform reimbursement agencies on health care interventions. Many clinical trials report outcomes using the frequency of an event over a set period of time, for example, the primary efficacy outcome in most clinical trials of migraine prevention is mean change in the frequency of migraine days (MDs) per 28 days (monthly MDs [MMD]) relative to baseline for active treatment versus placebo. Using these cohort-level endpoints in economic models, accounting for variation among patients is challenging. In this analysis, parametric models of change in MMD for migraine preventives were assessed using data from erenumab clinical studies. METHODS: MMD observations from the double-blind phases of two studies of erenumab were used: one in episodic migraine (EM) (NCT02456740) and one in chronic migraine (CM) (NCT02066415). For each trial, two longitudinal regression models were fitted: negative binomial and beta binomial. For a thorough comparison we also present the fitting from the standard multilevel Poisson and the zero inflated negative binomial. RESULTS: Using the erenumab study data, both the negative binomial and beta-binomial models provided unbiased estimates relative to observed trial data with well-fitting distribution at various time points. CONCLUSIONS: This proposed methodology, which has not been previously applied in migraine, has shown that these models may be suitable for estimating MMD frequency. Modelling MMD using negative binomial and beta-binomial distributions can be advantageous because these models can capture intra- and inter-patient variability so that trial observations can be modelled parametrically for the purposes of economic evaluation of migraine prevention. Such models have implications for use in a wide range of disease areas when assessing repeated measured utility values.

A Physical Education Intervention Effects on Correlates of Physical Activity and Motivation.

PURPOSE: The purpose of this study was to measure effects of a modified physical education (PE) unit on leisure time physical activity (LTPA), relative autonomy, and known correlates of LTPA in seventh-grade boys and girls. METHOD: A seventh-grade mountain biking unit was modified to include instructional activities targeting known correlates of PA behavior following principles of Physical Education Dedicated to Physical Activity for Life (PEDAL). A three-group design (intervention, standard PE, no PE) was employed. Participants completed a survey at baseline, postintervention, and follow-up at 4 weeks. RESULTS: A total of 300 seventh graders (girls = 151) from two schools completed the surveys. Data suggest PE may influence certain correlates of and autonomous motivation for PA although results revealed no intervention main effects for continuous and noncontinuous dependent variables. Results also provide evidence of sport-specific skill being improved through physical education. CONCLUSION: While results of this study showed no main effects from the intervention, data suggest PE may influence certain correlates of and autonomous motivation for PA. This warrants attention toward autonomy supporting PE environments and instruction sensitive to autonomous motivation. Future studies should examine PEDAL-designed PE programs over an entire year or more.

Characterising Uncertainty in the Assessment of Medical Devices and Determining Future Research Needs.

Decisions about the adoption of medical interventions are informed by evidence on their costs and effects. For a range of reasons, evidence relating to medical devices may be limited. The decision to adopt a device early in its life cycle when the evidence base is least mature may impact on the prospects of acquiring further evidence to reduce uncertainties. Equally, rejecting a device will result in no uptake in practice and hence no chance to learn about performance. Decision options such as 'only in research' or 'approval with research' can overcome these issues by allowing patients early access to promising new technologies while limiting the risks associated with making incorrect decisions until more evidence or learning is established. In this paper, we set out the issues relating to uncertainty and the value of research specific to devices: learning curve effects, incremental device innovation, investment and irrecoverable costs, and dynamic pricing. We show the circumstances under which an only in research or approval with research scheme may be an appropriate policy choice. We also consider how the value of additional research might be shared between the manufacturer and health sector to help inform who might reasonably be expected to conduct the research needed. © 2017 The Authors. Health Economics published by John Wiley & Sons, Ltd.

Effects of Implied Motion and Facing Direction on Positional Preferences in Single-Object Pictures.

Palmer, Gardner, and Wickens studied aesthetic preferences for pictures of single objects and found a strong inward bias: Right-facing objects were preferred left-of-center and left-facing objects right-of-center. They found no effect of object motion (people and cars showed the same inward bias as chairs and teapots), but the objects were not depicted as moving. Here we measured analogous inward biases with objects depicted as moving with an implied direction and speed by having participants drag-and-drop target objects into the most aesthetically pleasing position. In Experiment 1, human figures were shown diving or falling while moving forward or backward. Aesthetic biases were evident for both inward-facing and inward-moving figures, but the motion-based bias dominated so strongly that backward divers or fallers were preferred moving inward but facing outward. Experiment 2 investigated implied speed effects using images of humans, horses, and cars moving at different speeds (e.g., standing, walking, trotting, and galloping horses). Inward motion or facing biases were again present, and differences in their magnitude due to speed were evident. Unexpectedly, faster moving objects were generally preferred closer to frame center than slower moving objects. These results are discussed in terms of the combined effects of prospective, future-oriented biases, and retrospective, past-oriented biases.

RNA-Seq analysis of Gtf2ird1 knockout epidermal tissue provides potential insights into molecular mechanisms underpinning Williams-Beuren syndrome.

BACKGROUND: Williams-Beuren Syndrome (WBS) is a genetic disorder associated with multisystemic abnormalities, including craniofacial dysmorphology and cognitive defects. It is caused by a hemizygous microdeletion involving up to 28 genes in chromosome 7q11.23. Genotype/phenotype analysis of atypical microdeletions implicates two evolutionary-related transcription factors, GTF2I and GTF2IRD1, as prime candidates for the cause of the facial dysmorphology. RESULTS: Using a targeted Gtf2ird1 knockout mouse, we employed massively-parallel sequencing of mRNA (RNA-Seq) to understand changes in the transcriptional landscape associated with inactivation of Gtf2ird1 in lip tissue. We found widespread dysregulation of genes including differential expression of 78 transcription factors or coactivators, several involved in organ development including Hey1, Myf6, Myog, Dlx2, Gli1, Gli2, Lhx2, Pou3f3, Sox2, Foxp3. We also found that the absence of GTF2IRD1 is associated with increased expression of genes involved in cellular proliferation, including growth factors consistent with the observed phenotype of extreme thickening of the epidermis. At the same time, there was a decrease in the expression of genes involved in other signalling mechanisms, including the Wnt pathway, indicating dysregulation in the complex networks necessary for epidermal differentiation and facial skin patterning. Several of the differentially expressed genes have known roles in both tissue development and neurological function, such as the transcription factor Lhx2 which regulates several genes involved in both skin and brain development. CONCLUSIONS: Gtf2ird1 inactivation results in widespread gene dysregulation, some of which may be due to the secondary consequences of gene regulatory network disruptions involving several transcription factors and signalling molecules. Genes involved in growth factor signalling and cell cycle progression were identified as particularly important for explaining the skin dysmorphology observed in this mouse model. We have noted that a number of the dysregulated genes have known roles in brain development as well as epidermal differentiation and maintenance. Therefore, this study provides clues as to the underlying mechanisms that may be involved in the broader profile of WBS.

A trait-based approach for predicting species responses to environmental change from sparse data: how well might terrestrial mammals track climate change?

Estimating population spread rates across multiple species is vital for projecting biodiversity responses to climate change. A major challenge is to parameterise spread models for many species. We introduce an approach that addresses this challenge, coupling a trait-based analysis with spatial population modelling to project spread rates for 15 000 virtual mammals with life histories that reflect those seen in the real world. Covariances among life-history traits are estimated from an extensive terrestrial mammal data set using Bayesian inference. We elucidate the relative roles of different life-history traits in driving modelled spread rates, demonstrating that any one alone will be a poor predictor. We also estimate that around 30% of mammal species have potential spread rates slower than the global mean velocity of climate change. This novel trait-space-demographic modelling approach has broad applicability for tackling many key ecological questions for which we have the models but are hindered by data availability.

Estimating the Cost-Effectiveness of Implementation: Is Sufficient Evidence Available?

BACKGROUND: Timely implementation of recommended interventions can provide health benefits to patients and cost savings to the health service provider. Effective approaches to increase the implementation of guidance are needed. Since investment in activities that improve implementation competes for funding against other health generating interventions, it should be assessed in term of its costs and benefits. OBJECTIVE: In 2010, the National Institute for Health and Care Excellence released a clinical guideline recommending natriuretic peptide (NP) testing in patients with suspected heart failure. However, its implementation in practice was variable across the National Health Service in England. This study demonstrates the use of multi-period analysis together with diffusion curves to estimate the value of investing in implementation activities to increase uptake of NP testing. METHODS: Diffusion curves were estimated based on historic data to produce predictions of future utilization. The value of an implementation activity (given its expected costs and effectiveness) was estimated. Both a static population and a multi-period analysis were undertaken. RESULTS: The value of implementation interventions encouraging the utilization of NP testing is shown to decrease over time as natural diffusion occurs. Sensitivity analyses indicated that the value of the implementation activity depends on its efficacy and on the population size. CONCLUSIONS: Value of implementation can help inform policy decisions of how to invest in implementation activities even in situations in which data are sparse. Multi-period analysis is essential to accurately quantify the time profile of the value of implementation given the natural diffusion of the intervention and the incidence of the disease.

A Comprehensive Algorithm for Approval of Health Technologies With, Without, or Only in Research: The Key Principles for Informing Coverage Decisions.

BACKGROUND: The value of evidence about the performance of a technology and the value of access to a technology are central to policy decisions regarding coverage with, without, or only in research and managed entry (or risk-sharing) agreements. OBJECTIVES: We aim to outline the key principles of what assessments are needed to inform "only in research" (OIR) or "approval with research" (AWR) recommendations, in addition to approval or rejection. METHODS: We developed a comprehensive algorithm to inform the sequence of assessments and judgments that lead to different types of guidance: OIR, AWR, Approve, or Reject. This algorithm identifies the order in which assessments might be made, how similar guidance might be arrived at through different combinations of considerations, and when guidance might change. RESULTS: The key principles are whether the technology is expected to be cost-effective; whether the technology has significant irrecoverable costs; whether additional research is needed; whether research is possible with approval and whether there are opportunity costs that once committed by approval cannot be recovered; and whether there are effective price reductions. Determining expected cost-effectiveness is only a first step. In addition to AWR for technologies expected to be cost-effective and OIR for those not expected to be cost-effective, there are other important circumstances when OIR should be considered. CONCLUSIONS: These principles demonstrate that cost-effectiveness is a necessary but not sufficient condition for approval. Even when research is possible with approval, OIR may be appropriate when a technology is expected to be cost-effective due to significant irrecoverable costs.