Preclinical Data Extrapolation to Clinical Reality: A Translational Approach

In vivo investigations are much more complex than trials conducted in a test tube; the results sometimes aren't as illuminating and could raise more questions than answers. Preclinical data projection into clinical truth is a transcriptional science that remains a compelling trial in drug development. Preclinical in vivo and in vitro education is important in novel drug's non-violent or active growth. Pharmacokinetic and metabolic research is necessary to better understand the chemical and biological effects of medicines and their metabolites. Information produced by such a policy can be used to progress Phase I studies, primarily for anticancer medication. Both living and deceased in vitro models are theoretically excellent preclinical tools for calculating the pharmacological action of counterparts from the same family, such as vinca alkaloids. The animal species most closely linked to humans are chosen based on metabolic patterns. The estimation of the duration of drug action, particularly for medicines with varied metabolic clearances (e.g., benzodiazepines); The empathetic or estimate of medicine relations, i.e., those defined for cyclosporin A and macrolide antibiotics; and Sclarification of the metabolic roots of individual inconsistencies in pharmaceutical action.

Extrapolating animal consciousness

I argue that the question of animal consciousness is an extrapolation problem and, as such, is best tackled by deploying currently accepted methodology for validating experimental models of a phenomenon of interest. This methodology relies on an assessment of similarities and dissimilarities between experimental models, the partial replication of findings across complementary models, and evidence from the successes and failures of explanations, technologies and medical applications developed by extrapolating and aggregating findings from multiple models. Crucially important, this methodology does not require a commitment to any particular theory or construct of consciousness, thus avoiding theory-biased reinterpretations of empirical findings rampant in the literature.

A simulation study of extrapolation uncertainty in exposure assessment - Use of pilot study results for site investigation

Accurate characterization of soil contaminant concentrations is often crucial for assessing risks to human and ecological health. However, fine-scale assessments of large tracts of land can be cost prohibitive due to the number of samples needed. One solution to this problem is to extrapolate sampling results from one area to another unsampled area. In the absence of a validated extrapolation methodology, regulatory agencies have employed policy-based techniques for large sites, but the likelihood of decision errors resulting from these extrapolations is largely unexplored. This study describes the results of a simulation study aimed at guiding environmental sampling for sites where extrapolation concepts are of interest. The objective of this study is to provide practical recommendations to regulatory agencies for extrapolating sampling results on large tracts of land while minimizing errors that are detrimental to human health. A variety of site investigation scenarios representative of environmental conditions and sampling schemes were tested using adaptive sampling when collecting discrete samples or applying incremental sampling methodology (ISM). These simulations address extrapolation uncertainty in cases where a Pilot Study might result in either false noncompliance or false compliance conclusions. A wide range of plausible scenarios were used that reflect the variety of heterogeneity seen at large sites. This simulation study demonstrates that ISM can be reliably applied in a Pilot Study for purposes of extrapolating the outcome to a large area site because it decreases the likelihood of false non-compliance errors while also providing reliable estimates of true compliance across unsampled areas. The results demonstrate how errors depend on the magnitude of the 95% upper confidence limit for the mean concentration (95UCL) relative to the applicable action level, and that error rates are highest when the 95UCL is within 10%-40% of the action level. The false compliance rate can be reduced to less than 5% when 30% or more of the site is characterized with ISM. False compliance error rates using ISM are insensitive to the fraction of the decision units (DUs) that are characterized with three replicates (with a minimum of 10 percent), so long as 95UCLs are calculated for the DUs with one replicate using the average coefficient of variation from the three replicate DUs.

SurvInt: a simple tool to obtain precise parametric survival extrapolations

BACKGROUND

Economic evaluation of emerging health technologies is mandated by agencies such as the National Institute for Health and Care Excellence (NICE) to ensure their cost is proportional to their benefit. To avoid bias, NICE stipulate that the benefit of a treatment is assessed across the lifetime of the patient population, which can be many decades. Unfortunately, follow-up from a clinical trial will not usually cover the required period and the observed follow-up will require extrapolation. For survival data this is often done by selecting a preferred model from a set of candidate parametric models. This approach is limited in that the choice of model is restricted to those originally fitted. What if none of the models are consistent with clinical prediction or external data?

METHOD/RESULTS

This paper introduces SurvInt, a tool that estimates the parameters of common parametric survival models which interpolate key survival time co-ordinates specified by the user, which could come from external trials, real world data or expert clinical opinion. This is achieved by solving simultaneous equations based on the survival functions of the parametric models. The application of SurvInt is shown through two examples where traditional parametric modelling did not produce models that were consistent with external data or clinical opinion. Additional features include model averaging, mixture cure models, background mortality, piecewise modelling, restricted mean survival time estimation and probabilistic sensitivity analysis.

CONCLUSIONS

SurvInt allows precise parametric survival models to be estimated and carried forward into economic models. It provides access to extrapolations that are consistent with multiple data sources such as observed data and clinical predictions, opening the door to precise exploration of regions of uncertainty/disagreement. SurvInt could avoid the need for post-hoc adjustments for complications such as treatment switching, which are often applied to obtain a plausible survival model but at the cost of introducing additional uncertainty. Phase III clinical trials are not designed with extrapolation in mind, and so it is sensible to consider alternative approaches to predict future survival that incorporate external information.

Evidence of mechanisms in evidence-based policy

Evidence-based policy has achieved great relevance in policy-making and social research. Nonetheless, over the past few years, several problematic aspects of this approach have been identified. This paper discusses whether, and to what extent, evidence of mechanisms could contribute to addressing certain difficulties faced by evidence-based policy. I argue that it could play a crucial role in the assessment of the efficacy of interventions, the extrapolation of interventions to target populations, and the identification of side effects. For analysing the potential contribution of evidence of mechanisms, the previous debate on the pluralist approach to evidence-based medicine is taken as reference.

Challenges, approaches and enablers: effectively triangulating towards dose selection in pediatric rare diseases

Dose selection is an integral part of a molecule's journey to become medicine. On top of typical challenges faced in dose selection for more common diseases, pediatric rare disease has additional unique challenges due to the combination of 'rare' and 'pediatric' populations. Using the central theme of maximizing 'relevant' information to overcome information paucity, dose selection strategy in pediatric rare diseases is discussed using a triangulation concept involving challenges, approaches and very importantly, enablers. Using actual examples, unique scenarios are discussed where specific enablers allowed certain approaches to be used to overcome the challenges. The continued need for model-informed drug development is also discussed using examples of where modeling and simulation tools have been successfully used in bridging available information to select pediatric doses in rare disease. Additionally, challenges with translation and associated dose selection of new modalities such as gene therapy in rare diseases are examined with the lens of continuous learning and knowledge development that will enable pediatric dose selection of these modalities with confidence.

survextrap: a package for flexible and transparent survival extrapolation

BACKGROUND

Health policy decisions are often informed by estimates of long-term survival based primarily on short-term data. A range of methods are available to include longer-term information, but there has previously been no comprehensive and accessible tool for implementing these.

RESULTS

This paper introduces a novel model and software package for parametric survival modelling of individual-level, right-censored data, optionally combined with summary survival data on one or more time periods. It could be used to estimate long-term survival based on short-term data from a clinical trial, combined with longer-term disease registry or population data, or elicited judgements. All data sources are represented jointly in a Bayesian model. The hazard is modelled as an M-spline function, which can represent potential changes in the hazard trajectory at any time. Through Bayesian estimation, the model automatically adapts to fit the available data, and acknowledges uncertainty where the data are weak. Therefore long-term estimates are only confident if there are strong long-term data, and inferences do not rely on extrapolating parametric functions learned from short-term data. The effects of treatment or other explanatory variables can be estimated through proportional hazards or with a flexible non-proportional hazards model. Some commonly-used mechanisms for survival can also be assumed: cure models, additive hazards models with known background mortality, and models where the effect of a treatment wanes over time. All of these features are provided for the first time in an R package, survextrap, in which models can be fitted using standard R survival modelling syntax. This paper explains the model, and demonstrates the use of the package to fit a range of models to common forms of survival data used in health technology assessments.

CONCLUSIONS

This paper has provided a tool that makes comprehensive and principled methods for survival extrapolation easily usable.

A new methodology to extrapolate disease freedom to an area using surveillance results from selected aquatic populations

According to Chapter 1.4 of the World Organisation for Animal Health (WOAH) Aquatic Animal Health Code, an entire country or zone can be classified as free of a disease only if there is compelling evidence that all susceptible populations within the country or zone are free. However, the methods for achieving freedom are not prescribed in the WOAH standards and guidelines. Within this context, this paper describes a novel methodology to determine if surveillance results can be extrapolated from a study population to a target population. A framework of six criteria was developed to standardize a method for extrapolating surveillance results to other susceptible populations that have not been sampled. Criteria 1 assesses the internal validity for the freedom claim on the source population. Criteria 2 assesses which other susceptible populations have a non-negligible probability of exposure. Criteria 3 assesses whether the risk of infection upon exposure of the source population is the same or greater than each of the other susceptible populations. Finally, Criteria 4, 5 and 6 assess if the other susceptible populations would transmit the infection to the source population or if they have the same exposure pathways as the source population. We illustrate the use of this novel methodology using two hypothetical case scenarios. The presented methodology has the advantage of being applicable either retrospectively or prospectively. When applied retrospectively, it can be used to assess if the surveillance results of the source population can be extrapolated to the target population. When applied prospectively it can be used to design a more efficient surveillance system by selecting source populations from which it is easier to extrapolate surveillance results to the rest of the target population. Conclusions drawn using this methodology depend on the validity of the assumptions made when working through the methodology. We therefore recommend cautious application of the criteria and thorough review of all assumptions.

Consensus statement on the use of biosimilar drugs in immune-mediated diseases in Spain

OBJECTIVE

To improve knowledge about biosimilar medicines and to generate a consensus framework on their use.

METHODS

Qualitative study. A multidisciplinary group of experts in biosimilar medicines was established (1dermatologist, 1hospital pharmacist, 1rheumatologist, and 1gastroenterologist) who defined the sections and topics of the document. A narrative literature review was performed in Medline to identify articles on biosimilar medicines. Systematic reviews, controlled, pre-clinical, clinical, and real-life studies were selected. Based on the results of the review, several general principles and recommendations were generated. The level of agreement was tested in a Delphi that was extended to 66 health professionals who voted from 1 (totally disagree) to 10 (totally agree). Agreement was defined if at least 70% of the participants voted ≥7.

RESULTS

The literature review included 555 articles. A total of 10 general principles and recommendations were voted upon. All reached the level of agreement established. The document includes data on the main characteristics of biosimilar medicines (definition, development, approval, indication extrapolation, interchangeability, financing, and traceability); published evidence (biosimilarity, efficacy, effectiveness, safety, immunogenicity, efficiency, switch); barriers and facilitators to its use; and data on information for patients.

CONCLUSIONS

Authorized biosimilar medicines meet all the characteristics of quality, efficacy, and safety. They also significantly help improve patient access to biological therapies and contribute to health system sustainability.

Methodical advances in reproducibility research: A proof of concept qualitative comparative analysis of reproducing animal data in humans

BACKGROUND

While the term reproducibility crisis mainly reflects reproducibility of experiments between laboratories, reproducibility between species also remains problematic. We previously summarised the published reproducibility between animal and human studies; i.e. the translational success rates, which varied from 0% to 100%. Based on analyses of individual factors, we could not predict reproducibility. Several potential analyses can assess effect of combinations of predictors on an outcome. Regression analysis (RGA) is common, but not ideal to analyse multiple interactions and specific configurations (≈ combinations) of variables, which could be highly relevant to reproducibility. Qualitative comparative analysis (QCA) is based on set theory and Boolean algebra, and was successfully used in other fields. We reanalysed the data from our preceding review with QCA.

RESULTS

This QCA resulted in the following preliminary formula for successful translation: ∼Old*∼Intervention*∼Large*MultSpec*Quantitative Which means that within the analysed dataset, the combination of relative recency (∼ means not; >1999), analyses at event or study level (not at intervention level), n < 75, inclusion of more than one species and quantitative (instead of binary) analyses always resulted in successful translation (>85%). Other combinations of factors showed less consistent or negative results. An RGA on the same data did not identify any of the included variables as significant contributors.

CONCLUSIONS

While these data were not collected with the QCA in mind, they illustrate that the approach is viable and relevant for this research field. The QCA seems a highly promising approach to furthering our knowledge on between-species reproducibility.

Extrapolation of Survival Data Using a Bayesian Approach: A Case Study Leveraging External Data from Cilta-Cel Therapy in Multiple Myeloma

INTRODUCTION

Extrapolating long-term overall survival (OS) from shorter-term clinical trial data is key to health technology assessment in oncology. However, extrapolation using conventional methods is often subject to uncertainty. Using ciltacabtagene autoleucel (cilta-cel), a chimeric antigen receptor T-cell therapy for multiple myeloma, we used a flexible Bayesian approach to demonstrate use of external longer-term data to reduce the uncertainty in long-term extrapolation.

METHODS

The pivotal CARTITUDE-1 trial (NCT03548207) provided the primary efficacy data for cilta-cel, including a 12-month median follow-up snapshot of OS. Longer-term (48-month median follow-up) survival data from the phase I LEGEND-2 study (NCT03090659) were also available. Twelve-month CARTITUDE-1 OS data were extrapolated in two ways: (1) conventional survival models with standard parametric distributions (uninformed), and (2) Bayesian survival models whose shape prior was informed from 48-month LEGEND-2 data. For validation, extrapolations from 12-month CARTITUDE-1 data were compared with observed 28-month CARTITUDE-1 data.

RESULTS

Extrapolations of the 12-month CARTITUDE-1 data using conventional uninformed parametric models were highly variable. Using informative priors from the 48-month LEGEND-2 dataset, the ranges of projected OS at different timepoints were consistently narrower. Area differences between the extrapolation curves and the 28-month CARTITUDE-1 data were generally lower in informed Bayesian models, except for the uninformed log-normal model, which had the lowest difference.

CONCLUSIONS

Informed Bayesian survival models reduced variation of long-term projections and provided similar projections as the uninformed log-normal model. Bayesian models generated a narrower and more plausible range of OS projections from 12-month data that aligned with observed 28-month data.

TRIAL REGISTRATION

CARTITUDE-1 ClinicalTrials.gov identifier, NCT03548207. LEGEND-2 ClinicalTrials.gov identifier, NCT03090659, registered retrospectively on 27 March 2017, and ChiCTR-ONH-17012285.

INES: Interactive tool for construction and extrapolation of partitioned survival models

BACKGROUND

INES (INteractive model for Extrapolation of Survival and cost) provides an open-access tool powered by R that implements three-state partitioned survival models (PSM). This article describes the properties of the tool, and the situations where INES may or may not be suitable.

METHODS

INES is designed to be used by investigators or healthcare professionals who have a good grasp of the principles of economic evaluation and understand the strengths and weaknesses of partitioned survival models, but are not sufficiently familiar with a statistical package such as Excel or R to be able to construct and test a de-novo PSM themselves. INES is delivered to the user via a batch file. Once downloaded to the user's hard drive, it interacts with the user via a portable version of R with web interactivity built in Shiny. INES requires absolutely no knowledge of R and the user does not need to have R or any of its dependences installed. Hence the user will deal with a standalone Shiny app. Inputs (digitalized survival curves, unit costs, posology, hazard ratios, discount rate) can be uploaded from a template spreadsheet.

RESULTS

The INES application provides a seamlessly integrated package for estimating a set of parametric hazard functions for progression free and overall survival, selecting an appropriate function from this menu, and applying this as an input to a PSM to calculate mean costs and quality-adjusted life years. Examples are given that may serve as a tutorial.

CONCLUSION

INES offers a rapid, flexible, robust and transparent tool for parametric survival analysis and calculating a PSM that can be used in many different contexts.

Incidence, mortality and trends of cutaneous squamous cell carcinoma in Germany, the Netherlands, and Scotland

AIM OF THE STUDY

Cutaneous squamous cell carcinoma (cSCC) incidences are increasing but scarcely available separated. We analysed incidence rates of cSCC over three decades with an extrapolation to 2040.

METHODS

Cancer registries from the Netherlands, Scotland and two federal states of Germany (Saarland/Schleswig-Holstein) were sourced for separate cSCC incidence data. Incidence and mortality trends between 1989/90 and 2020 were assessed using Joinpoint regression models. Modified age-period-cohort models were applied to predict incidence rates up to 2044. Rates were age-standardised using the new European standard population (2013).

RESULTS

Age-standardised incidence rates (ASIR, per 100,000 persons per year) increased in all populations. The annual percent increase ranged between 2.4% and 5.7%. The highest increase occurred in the age groups ≥60 years, especially in men aged ≥80 years, with a three to 5-fold increase. Extrapolations up to 2044 showed an unrestrained increase in incidence rates in all countries investigated. Age-standardised mortality rates (ASMR) showed slight increases between 1.4 and 3.2% per year in Saarland and Schleswig-Holstein for both sexes and for men in Scotland. For the Netherlands, ASMRs remained stable for women but declined for men.

CONCLUSION

There was a continuous increase of cSCC incidence over three decades with no tendency for levelling-off, especially in the older populations as males ≥80 years. Extrapolations point to a further increasing number of cSCC up to 2044, especially among ≥60 years. This will have a significant impact on the current and future burden on dermatologic health care which will be faced with major challenges.

Projecting overall survival in health-economic models: uncertainty and maturity of data

OBJECTIVE

As lifetime horizons are considered for economic evaluations, the Kaplan-Meier (KM) estimate is used to extrapolate survival in cases of immature overall survival (OS) data. This study estimated the error induced by the choice of distribution when extrapolating different levels of OS maturity.

METHODS

Fifteen phase 3 trials reporting KM estimates of OS where at least 70% maturity (i.e. 70% of the population had died during follow-up) were included and compared to artificially created truncated data (30 and 50% maturity). Individual patient-data were reproduced using the Guyot algorithm based on digitized KM curves. Parametric survival distributions were fit for each arm in each study, for each maturity level, using the same time horizon (equal to the maximum follow-up). For each KM curve, the best distribution was chosen based on visual inspection, Akaike/Bayesian information criteria, and external validity. Outcomes were measured as life expectancy in months (LM) and life months gained (LMG).

RESULTS

The Weibull (33%), log-logistic (32%) and log-normal (27%) were most often selected as the best fitting distribution. Compared to LM at full maturity, LM was overestimated in 23 and 40% of cases, at 30 and 50% maturity, respectively. Mean absolute error was 2.12months at 30% maturity, and decreased to 0.88months at 50% maturity. When comparing to mature data, the mean percentage of error in LMG was 126.4 and 62.4% at 30 and 50% maturity, respectively.

CONCLUSION

The extent of OS maturity increases the risk of error when projecting long-term life expectancy for economic models. Even marginal gains in OS maturity result in more accurate estimations and should be considered when developing models.

Predictability and transferability of local biodiversity environment relationships

Background

Biodiversity varies in space and time, and often in response to environmental heterogeneity. Indicators in the form of local biodiversity measures-such as species richness or abundance-are common tools to capture this variation. The rise of readily available remote sensing data has enabled the characterization of environmental heterogeneity in a globally robust and replicable manner. Based on the assumption that differences in biodiversity measures are generally related to differences in environmental heterogeneity, these data have enabled projections and extrapolations of biodiversity in space and time. However so far little work has been done on quantitatively evaluating if and how accurately local biodiversity measures can be predicted.

Methods

Here I combine estimates of biodiversity measures from terrestrial local biodiversity surveys with remotely-sensed data on environmental heterogeneity globally. I then determine through a cross-validation framework how accurately local biodiversity measures can be predicted within ("predictability") and across similar ("transferability") biodiversity surveys.

Results

I found that prediction errors can be substantial, with error magnitudes varying between different biodiversity measures, taxonomic groups, sampling techniques and types of environmental heterogeneity characterizations. And although errors associated with model predictability were in many cases relatively low, these results question-particular for transferability-our capability to accurately predict and project local biodiversity measures based on environmental heterogeneity. I make the case that future predictions should be evaluated based on their accuracy and inherent uncertainty, and ecological theories be tested against whether we are able to make accurate predictions from local biodiversity data.

On automatic bias reduction for extreme expectile estimation

Expectiles induce a law-invariant risk measure that has recently gained popularity in actuarial and financial risk management applications. Unlike quantiles or the quantile-based Expected Shortfall, the expectile risk measure is coherent and elicitable. The estimation of extreme expectiles in the heavy-tailed framework, which is reasonable for extreme financial or actuarial risk management, is not without difficulties; currently available estimators of extreme expectiles are typically biased and hence may show poor finite-sample performance even in fairly large samples. We focus here on the construction of bias-reduced extreme expectile estimators for heavy-tailed distributions. The rationale for our construction hinges on a careful investigation of the asymptotic proportionality relationship between extreme expectiles and their quantile counterparts, as well as of the extrapolation formula motivated by the heavy-tailed context. We accurately quantify and estimate the bias incurred by the use of these relationships when constructing extreme expectile estimators. This motivates the introduction of classes of bias-reduced estimators whose asymptotic properties are rigorously shown, and whose finite-sample properties are assessed on a simulation study and three samples of real data from economics, insurance and finance.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11222-022-10118-x.

Determining macrophyte species richness and dark diversity sources - A novel approach to improve the biodiversity estimation based on species traits

Biodiversity measures deliver valuable ecological information by reflecting a range of ecosystem processes. However, the accuracy of environmental assessment based on species patterns may often be affected by insufficient survey details. The comprehensive evaluation of plant taxa richness in rivers requires an extensive sampling effort. The use of Hill numbers and Chao estimators improves species diversity assessment based on a feasible number of samples. The aim of this work was to identify macrophyte groups, associated with various species traits, which are rich in species, as well as those whose detection is particularly difficult as it requires an exceptional sampling effort (sources of dark diversity). Analyses were performed with the use of Hill numbers and Chao estimators. It was shown that the field identification of all estimated macrophytes is particularly difficult for low trophy indicators and generally submerged plants, as well as for small-leaved species. A field survey encompassing the full (expected) macrophyte diversity encountered within a river is easiest to perform in the case of free-floating plants and large-leaved macrophytes, as well as for species with high trophic tolerance. The study proved that ecological assessment of rivers based on a small number of sampling units may lead to incorrect diversity estimates. Conversely, the estimation of diversity patterns at the level of the Shannon and Simpson indices does not require extensive sampling, and the extrapolation approach is not needed. The effectiveness of diversity assessment in fluvial ecosystems can be increased by extrapolation of gray diversity which can be considered in planning of monitoring programs. Moreover even estimated dark diversity bight be already efficient to identify ecological pattern and when comparing biodiversity across regions and ecosystems.

Analyzing the misperception of exponential growth in graphs

Exponential growth is frequently underestimated, an error that can have a heavy social cost in the context of epidemics. To clarify its origins, we measured the human capacity (N = 521) to extrapolate linear and exponential trends in scatterplots. Four factors were manipulated: the function underlying the data (linear or exponential), the response modality (pointing or venturing a number), the scale on the y axis (linear or logarithmic), and the amount of noise in the data. While linear extrapolation was precise and largely unbiased, we observed a consistent underestimation of noisy exponential growth, present for both pointing and numerical responses. A biased ideal-observer model could explain these data as an occasional misperception of noisy exponential graphs as quadratic curves. Importantly, this underestimation bias was mitigated by participants' math knowledge, by using a logarithmic scale, and by presenting a noiseless exponential curve rather than a noisy data plot, thus suggesting concrete avenues for interventions.

A Review of Overall Survival Extrapolations of Immune-Checkpoint Inhibitors Used in Health Technology Assessments by the French Health Authorities

OBJECTIVES

Extrapolation is often required to inform cost-effectiveness (CE) evaluations of immune-checkpoint inhibitors (ICIs) since survival data from pivotal clinical trials are seldom complete. The objectives of this study were to evaluate the accuracy of estimates of long-term overall survival (OS) predicted in French CE assessment reports of ICIs, and to identify models presenting the best fit to the observed long-term survival data.

METHODS

A systematic review of French assessment reports of ICIs in the metastatic setting since inception until May 2020 was performed. A targeted literature review was conducted to collect associated extended follow-up of randomized controlled trials (RCTs) used in the CE assessment reports. Difference between projected and observed OS was calculated. A range of standard parametric and spline-based models were applied to the extended follow-up data from the RCT to determine the best-fitting survival models.

RESULTS

Of the 121 CE assessment reports published, 11 reports met the inclusion criteria. OS was underestimated in 73 percent of the CE assessment reports. The mean relative difference between each source was -13 percent (median: -15 percent; IQR: -0.4 to 26 percent). Models providing the best fit were those that could reflect nonmonotonic hazards.

CONCLUSIONS

Based on the available data at the time of submission, longer-term survival of ICIs was not fully captured by the extrapolation models used in CE assessments. Standard and flexible parametric models which can capture nonmonotonic hazard functions provided the best fit to the extended follow-up data. However, these models may still have performed poorly if fitted to survival data available at the time of submission to the French National Authority for Health.

An Introduction to Biosimilars for the Treatment of Retinal Diseases: A Narrative Review

Biological therapies have revolutionized the treatment of disease across a number of therapeutic areas including retinal diseases. However, on occasion, such treatments may be relatively more expensive compared to small molecule therapies. This can restrict patient access and treatment length leading to suboptimal clinical outcomes. Several biosimilar candidates of ranibizumab and aflibercept are currently in development and the first biosimilar of ranibizumab received EMA approval in August and FDA approval in September 2021. Biosimilars are biological medicines that are highly similar to an already-approved biological medicine (reference product). The physicochemical and clinical similarity of a biosimilar is determined by a rigorous analytical and clinical program, including extensive pharmacokinetic and pharmacodynamic analysis with phase III equivalence studies where appropriate. These phase III studies are carried out in a patient population that is representative of all of the potential approved therapeutic indications of the originator product and the most sensitive for detecting potential differences between the biosimilar and the reference product. Biosimilars have been used successfully across a wide range of therapeutic areas for the past 15 years where they have achieved substantial cost savings that can be reinvested into healthcare systems without affecting the quality of patient care. The current review provides an introduction to biosimilars with the aim of preparing retinal specialists for discussing these products with their patients.

Using Data on Survival with Idiopathic Pulmonary Fibrosis to Estimate Survival with Other Types of Progressive Fibrosis Interstitial Lung Disease: A Bayesian Framework

INTRODUCTION

Among the various types of progressive fibrosing interstitial lung diseases (PF-ILDs), substantial survival data exist for idiopathic pulmonary fibrosis (IPF) but not for other types. This hinders evidence-based decisions about treatment and management, as well as the economic modelling needed to justify research into new treatments and reimbursement approvals. Given the clinical similarities between IPF and other PF-ILDs, we reasoned that patient survival data from four major IPF trials could be used to estimate long-term survival in other PF-ILDs.

METHODS

We used propensity score matching to match patients with IPF taking either nintedanib or placebo in the TOMORROW, INPULSIS-1, INPULSIS-2 and INPULSIS-ON trials to patients with PF-ILDs other than IPF in the INBUILD trial. Seven models were fitted to the survival data for the matched patients with IPF, and the three best-fitting models were used to generate informative priors in a Bayesian framework to extrapolate patient survival of the INBUILD population.

RESULTS

After propensity score matching, the analysis included data from 1099 patients with IPF (640 nintedanib patients; 459 placebo patients) and 654 patients with other PF-ILDs (326 nintedanib patients; 328 placebo patients). Gamma, log-logistic and Weibull models best fit the survival of the matched patients with IPF. All three models led to consistent Bayesian estimates of survival for the matched patients with other PF-ILDs, with median rates of overall survival ranging from 6.34 to 6.50 years after starting nintedanib. The corresponding control group survival estimates were 3.42 to 3.76 years.

CONCLUSION

We provide the first estimates of long-term overall survival for patients with PF-ILDs other than IPF, and our analysis suggests that nintedanib may prolong their survival. Our Bayesian approach to estimating survival of one disease based on clinical trial data from a similar disease may help inform economic modelling of rare, orphan and newly defined disorders.

Extrapolation as a Default Strategy in Pediatric Drug Development

Pediatric drug development lags adult development by about 8 years (Mulugeta et al. in Pediatr Clin 64(6):1185-1196, 2017). In such context, many incentives, regulations, and innovative techniques have been proposed to address the disparity for pediatric patients. One such strategy is extrapolation of efficacy from a reference population. Extrapolation is currently justified by providing evidence in support of the effective use of drugs in children when the course of the disease and the expected treatment response would be sufficiently similar in the pediatric and reference population. This paper's position is that, despite uncertainties, pediatric drug development programs should initially assume some degree of extrapolation. The degree to which extrapolation can be used lies along a continuum representing the uncertainties to be addressed through generation of new pediatric evidence. In addressing these uncertainties, the extrapolation strategy should reflect the level of tolerable uncertainty concerning the decision to expose a child to the risks of a new drug. This judgment about the level of tolerable uncertainty should vary with the context (e.g., disease severity, existing therapeutic options) and can be embedded into pediatric drug development archetypes to ascertain the extent of studies needed and whether simultaneous development for adults and adolescents be considered.

Determination of the aqueous pKa of very insoluble drugs by capillary electrophoresis: Internal standards for methanol-water extrapolation

A fast determination of acidity constants (pK_a) of very insoluble drugs has become a necessity in drug discovery process because it often produces molecules that are highly lipophilic and sparingly soluble in water. In this work the high throughput internal standard capillary electrophoresis (IS-CE) method has been adapted to the determination of pK_a of water insoluble compounds by measurement in methanol/aqueous buffer mixtures. For this purpose, the reference pK_a values for a set of 46 acid-base compounds of varied structure (internal standards) have been established in methanol-water mixtures at several solvent composition levels (with a maximum of 40% methanol). The IS-CE method has been successfully applied to seven test drugs of different chemical nature with intrinsic solubilities lower than 10^-6 M. pK_a values have been determined at different methanol/aqueous buffer compositions and afterwards Yasuda-Shedlovsky extrapolation method has been applied to obtain the aqueous pK_a. The obtained results have successfully been compared to literature ones obtained by other methods. It is concluded that the IS-CE method allows the determination of aqueous pK_a values using low proportions of methanol, becoming then more accurate in the extrapolation procedure than other reference methods.

Innovative Pediatric Development for Secukinumab in Psoriasis: Faster Patient Access, Reduction of Patients on Control

This manuscript describes the innovative pediatric development strategy of secukinumab in psoriasis. Initially, this pediatric development had been planned early during the adult development program. This is common for most development programs as it is required by law in major regions worldwide. At that time, only limited experience with secukinumab's mechanism of action was available, especially in the pediatric population for which no data had been collected yet. Pediatric extrapolation frameworks were not in use. As a result, a standard pediatric program had initially been proposed requiring two relatively large placebo-controlled pivotal trials. However, as experience with the use of secukinumab grew, so did experience with - and regulatory openness to - innovative quantitative approaches such as extrapolation. It became clear that the pediatric development of secukinumab could be substantially accelerated by these novel ideas. Here we report how these concepts were implemented in the pediatric development program of secukinumab in psoriasis, using a wide range of innovative quantitative techniques. This strategy led to the removal of the placebo arm from one trial, a substantial reduction of the overall sample size in this program, and a significantly faster approval of secukinumab for the pediatric psoriasis population.

Impact of limited sample size and follow-up on single event survival extrapolation for health technology assessment: a simulation study

INTRODUCTION

Extrapolation of time-to-event data from clinical trials is commonly used in decision models for health technology assessment (HTA). The objective of this study was to assess performance of standard parametric survival analysis techniques for extrapolation of time-to-event data for a single event from clinical trials with limited data due to small samples or short follow-up.

METHODS

Simulated populations with 50,000 individuals were generated with an exponential hazard rate for the event of interest. A scenario consisted of 5000 repetitions with six sample size groups (30-500 patients) artificially censored after every 10% of events observed. Goodness-of-fit statistics (AIC, BIC) were used to determine the best-fitting among standard parametric distributions (exponential, Weibull, log-normal, log-logistic, generalized gamma, Gompertz). Median survival, one-year survival probability, time horizon (1% survival time, or 99th percentile of survival distribution) and restricted mean survival time (RMST) were compared to population values to assess coverage and error (e.g., mean absolute percentage error).

RESULTS

The true exponential distribution was correctly identified using goodness-of-fit according to BIC more frequently compared to AIC (average 92% vs 68%). Under-coverage and large errors were observed for all outcomes when distributions were specified by AIC and for time horizon and RMST with BIC. Error in point estimates were found to be strongly associated with sample size and completeness of follow-up. Small samples produced larger average error, even with complete follow-up, than large samples with short follow-up. Correctly specifying the event distribution reduced magnitude of error in larger samples but not in smaller samples.

CONCLUSIONS

Limited clinical data from small samples, or short follow-up of large samples, produce large error in estimates relevant to HTA regardless of whether the correct distribution is specified. The associated uncertainty in estimated parameters may not capture the true population values. Decision models that base lifetime time horizon on the model's extrapolated output are not likely to reliably estimate mean survival or its uncertainty. For data with an exponential event distribution, BIC more reliably identified the true distribution than AIC. These findings have important implications for health decision modelling and HTA of novel therapies seeking approval with limited evidence.

Comparing current and emerging practice models for the extrapolation of survival data: a simulation study and case-study

BACKGROUND

Estimates of future survival can be a key evidence source when deciding if a medical treatment should be funded. Current practice is to use standard parametric models for generating extrapolations. Several emerging, more flexible, survival models are available which can provide improved within-sample fit. This study aimed to assess if these emerging practice models also provided improved extrapolations.

METHODS

Both a simulation study and a case-study were used to assess the goodness of fit of five classes of survival model. These were: current practice models, Royston Parmar models (RPMs), Fractional polynomials (FPs), Generalised additive models (GAMs), and Dynamic survival models (DSMs). The simulation study used a mixture-Weibull model as the data-generating mechanism with varying lengths of follow-up and sample sizes. The case-study was long-term follow-up of a prostate cancer trial. For both studies, models were fit to an early data-cut of the data, and extrapolations compared to the known long-term follow-up.

RESULTS

The emerging practice models provided better within-sample fit than current practice models. For data-rich simulation scenarios (large sample sizes or long follow-up), the GAMs and DSMs provided improved extrapolations compared with current practice. Extrapolations from FPs were always very poor whilst those from RPMs were similar to current practice. With short follow-up all the models struggled to provide useful extrapolations. In the case-study all the models provided very similar estimates, but extrapolations were all poor as no model was able to capture a turning-point during the extrapolated period.

CONCLUSIONS

Good within-sample fit does not guarantee good extrapolation performance. Both GAMs and DSMs may be considered as candidate extrapolation models in addition to current practice. Further research into when these flexible models are most useful, and the role of external evidence to improve extrapolations is required.

(Eco)toxicological tests for assessing impacts of chemical stress to aquatic ecosystems: Facts, challenges, and future

Monitoring of chemicals in the aquatic environment by chemical analysis alone cannot completely assess and predict the effects of chemicals on aquatic species and ecosystems. This is primarily because of the increasing number of (unknown) chemical stressors and mixture effects present in the environment. In addition, the ability of ecological indices to identify underlying stressors causing negative ecological effects is limited. Therefore, additional complementary methods are needed that can address the biological effects in a direct manner and provide a link to chemical exposure, i.e. (eco)toxicological tests. (Eco)toxicological tests are defined as test systems that expose biological components (cells, individuals, populations, communities) to (environmental mixtures of) chemicals to register biological effects. These tests measure responses at the sub-organismal (biomarkers and in vitro bioassays), whole-organismal, population, or community level. We performed a literature search to obtain a state-of-the-art overview of ecotoxicological tests available for assessing impacts of chemicals to aquatic biota and to reveal datagaps. In total, we included 509 biomarkers, 207 in vitro bioassays, 422 tests measuring biological effects at the whole-organismal level, and 78 tests at the population- community- and ecosystem-level. Tests at the whole-organismal level and biomarkers were most abundant for invertebrates and fish, whilst in vitro bioassays are mostly based on mammalian cell lines. Tests at the community- and ecosystem-level were almost missing for organisms other than microorganisms and algae. In addition, we provide an overview of the various extrapolation challenges faced in using data from these tests and suggest some forward looking perspectives. Although extrapolating the measured responses to relevant protection goals remains challenging, the combination of ecotoxicological experiments and models is key for a more comprehensive assessment of the effects of chemical stressors to aquatic ecosystems.

Flexible prediction of opponent motion with internal representation in interception behavior

Skilled interception behavior often relies on accurate predictions of external objects because of a large delay in our sensorimotor systems. To deal with the sensorimotor delay, the brain predicts future states of the target based on the current state available, but it is still debated whether internal representations acquired from prior experience are used as well. Here we estimated the predictive manner by analyzing the response behavior of a pursuer to a sudden directional change of the evasive target, providing strong evidence that prediction of target motion by the pursuer was incompatible with a linear extrapolation based solely on the current state of the target. Moreover, using neural network models, we validated that nonlinear extrapolation as estimated was computationally feasible and useful even against unknown opponents. These results support the use of internal representations in predicting target motion, suggesting the usefulness and versatility of predicting external object motion through internal representations.

Satellite-based modelling of potential tsetse (Glossina pallidipes) breeding and foraging sites using teneral and non-teneral fly occurrence data

BACKGROUND

African trypanosomiasis, which is mainly transmitted by tsetse flies (Glossina spp.), is a threat to public health and a significant hindrance to animal production. Tools that can reduce tsetse densities and interrupt disease transmission exist, but their large-scale deployment is limited by high implementation costs. This is in part limited by the absence of knowledge of breeding sites and dispersal data, and tools that can predict these in the absence of ground-truthing.

METHODS

In Kenya, tsetse collections were carried out in 261 randomized points within Shimba Hills National Reserve (SHNR) and villages up to 5 km from the reserve boundary between 2017 and 2019. Considering their limited dispersal rate, we used in situ observations of newly emerged flies that had not had a blood meal (teneral) as a proxy for active breeding locations. We fitted commonly used species distribution models linking teneral and non-teneral tsetse presence with satellite-derived vegetation cover type fractions, greenness, temperature, and soil texture and moisture indices separately for the wet and dry season. Model performance was assessed with area under curve (AUC) statistics, while the maximum sum of sensitivity and specificity was used to classify suitable breeding or foraging sites.

RESULTS

Glossina pallidipes flies were caught in 47% of the 261 traps, with teneral flies accounting for 37% of these traps. Fitted models were more accurate for the teneral flies (AUC = 0.83) as compared to the non-teneral (AUC = 0.73). The probability of teneral fly occurrence increased with woodland fractions but decreased with cropland fractions. During the wet season, the likelihood of teneral flies occurring decreased as silt content increased. Adult tsetse flies were less likely to be trapped in areas with average land surface temperatures below 24 °C. The models predicted that 63% of the potential tsetse breeding area was within the SHNR, but also indicated potential breeding pockets outside the reserve.

CONCLUSION

Modelling tsetse occurrence data disaggregated by life stages with time series of satellite-derived variables enabled the spatial characterization of potential breeding and foraging sites for G. pallidipes. Our models provide insight into tsetse bionomics and aid in characterising tsetse infestations and thus prioritizing control areas.

Applying State-of-the-Art Survival Extrapolation Techniques to the Evaluation of CAR-T Therapies: Evidence from a Systematic Literature Review

INTRODUCTION

Traditional statistical techniques for extrapolating short-term survival data for anticancer therapies assume the same mortality rate for noncured and "cured" patients, which is appropriate for projecting survival of non-curative therapies but may lead to an underestimation of the treatment effectiveness for potentially curative therapies. Our objective was to ascertain research trends in survival extrapolation techniques used to project the survival benefits of chimeric antigen receptor T cell (CAR-T) therapies.

METHODS

A global systematic literature search produced a review of survival analyses of CAR-T therapies, published between January 1, 2015 and December 14, 2020, based on publications sourced from MEDLINE, scientific conferences, and health technology assessment agencies. Trends in survival extrapolation techniques used, and the rationale for selecting advanced techniques, are discussed.

RESULTS

Twenty publications were included, the majority of which (65%, N = 13) accounted for curative intent of CAR-T therapies through the use of advanced extrapolation techniques, i.e., mixture cure models [MCMs] (N = 10) or spline-based models (N = 3). The authors' rationale for using the MCM approach included (a) better statistical fits to the observed Kaplan-Meier curves (KMs) and (b) visual inspection of the KMs indicated that a proportion of patients experienced long-term remission and survival which is not inherently captured in standard parametric distributions.

DISCUSSION

Our findings suggest that an advanced extrapolation technique should be considered in base case survival analyses of CAR-T therapies when extrapolating short-term survival data to long-term horizons extending beyond the clinical trial duration.

CONCLUSION

Advanced extrapolation techniques allow researchers to account for the proportion of patients with an observed plateau in survival from clinical trial data; by only using standard-partitioned modeling, researchers may risk underestimating the survival benefits for the subset of patients with long-term remission. Sensitivity analysis with an alternative advanced extrapolation technique should be implemented and re-assessment using clinical trial extension data and/or real-world data should be conducted as longer-term data become available.

Can humans perform mental regression on a graph? Accuracy and bias in the perception of scatterplots

Despite the widespread use of graphs, little is known about how fast and how accurately we can extract information from them. Through a series of four behavioral experiments, we characterized human performance in "mental regression", i.e. the perception of statistical trends from scatterplots. When presented with a noisy scatterplot, even as briefly as 100 ms, human adults could accurately judge if it was increasing or decreasing, fit a regression line, and extrapolate outside the original data range, for both linear and non-linear functions. Performance was highly consistent across those three tasks of trend judgment, line fitting and extrapolation. Participants' linear trend judgments took into account the slope, the noise, and the number of data points, and were tightly correlated with the t-test classically used to evaluate the significance of a linear regression. However, they overestimated the absolute value of the regression slope. This bias was inconsistent with ordinary least squares (OLS) regression, which minimizes the sum of square deviations, but consistent with the use of Deming regression, which treats the x and y axes symmetrically and minimizes the Euclidean distance to the fitting line. We speculate that this fast but biased perception of scatterplots may be based on a "neuronal recycling" of the human visual capacity to identify the medial axis of a shape.

Extrapolation: Experience gained from original biologics

Biologicals undergo modifications throughout their commercial lifecycle. Major changes can unintentionally magnify their inherent physicochemical variability. Although trials comparing the pre- and the post-change versions have been requested occasionally, analytical comparison is the most sensitive approach to anticipating clinical equivalence. Therefore, it may be concluded, by means of 'extrapolation', that non-identical versions of a given biologic will behave equally in all indications. Despite the lessons learned with original biologics, there are still controversies around the approval of biosimilars through extrapolation. Here, a comprehensive analysis of scattered information allows for an account of cases of original biologic versions approved in some indications with no patient trials involved. Healthcare professionals can be reassured that inasmuch as extrapolation has proven valid for new versions of original biologics, the same holds for biosimilars.

60 years of absolute standardization of radionuclides by coincidence counting methods in the Romanian metrology laboratory

This review paper presents the contribution of the Radionuclide Metrology Laboratory (RML) to the development and practical application of the coincidence counting methods. It is also intended to pay tribute to Dr. Enric Leon Grigorescu, the founder of our school of radionuclide metrology. A synthetic treatment of the method, applied to some classes of radionuclides with their common and particular aspects in standardization, is presented: pioneering calculation of instrumental corrections, specific method corrections applied for various types of decay schemes in the variant of efficiency extrapolation, results obtained in international comparisons.

Generalized gene expression programming models for estimating reference evapotranspiration through cross-station assessment and exogenous data supply

Adopting methodologies utilizing exogenous data from ancillary stations for determining crop water requirement is a suitable approach to exempt local shortcomings due to the lack of meteorological data/stations. Meanwhile, soft computing techniques might be suitable tools to be used with such data management scenarios. The present paper aimed at evaluating the generalizability of the gene expression programming (GEP) technique for estimating reference evapotranspiration (ET₀) through cross-station assessment and exogenous data supply, using data from Turkey and Iran. The GEP-based models were established and learnt using data from 10 stations in Turkey, and then the developed models were tested (validated) in 18 stations of Iran with considerable latitude differences. Different time periods (beginning and the end of time series) were selected for the training and testing stations so that there was no overlap among the dates of the events in both the groups. A comparison was also performed between the GEP models and the corresponding commonly used empirical equations. The obtained results revealed that the generalized GEP models presented promising outcomes in simulating daily ET₀ values when they were trained and tested in quite distant stations with different chronological periods of the applied parameters. The performance accuracy of the empirical equations calibrated using exogenous data was reduced in comparison with their original (non-calibrated) versions. Further, although the generalization ability of the GEP models was reduced when the climatic context of the training-testing stations was different, the overall performance accuracy of those models was higher than those of the commonly used classic empirical equations.

Evaluation of different approaches to applying the standard additions calibration method

The extrapolation approach, traditionally used with standard additions (SA), is compared with the alternative strategies of interpolation, reversed-axis, and normalization. The interpolation approach is based on employing twice the analytical signal recorded for the sample (y_sam) to determine an unknown analyte concentration. In the reversed-axis strategy, x- and y-axes are swapped when building the SA calibration plot to facilitate uncertainty estimation. A new strategy, based on signal normalization using y_sam, is also described and compared to the other approaches. Results from 3 instrumental methods, 396 sample replicates, 16 analytes, and 2 certified reference materials are included in this study. For most applications, all four SA approaches provide statistically similar trueness and precision. However, extrapolation and reversed-axis provide more consistent values (within narrower ranges) than the other strategies when employing inductively coupled plasma optical emission spectrometry (ICP OES). On the other hand, normalization provides better trueness for the less robust method of microwave-induced plasma OES (MIP OES), as it is capable of minimizing systematic errors associated with different points of the calibration curve. Normalization is particularly useful for quickly processing data, without the need for inspecting each individual calibration plot to identify outlying points. Reversed-axis and normalization are the most adequate approaches for SA applications involving MIP OES and ICP-based methods. In addition to providing similar accuracies to the traditional extrapolation approach, these strategies present the advantage of a simple uncertainty estimation, which can be easily calculated using commonly available software such as Microsoft Excel and R.

Kidney dosimetry in 777 patients during 177Lu-DOTATATE therapy: aspects on extrapolations and measurement time points

Purpose

Fractionated peptide receptor radionuclide therapy (PRRT) with ¹⁷⁷Lu-DOTATATE is increasingly applied as an effective treatment for patients with disseminated neuroendocrine tumors. In parallel to dose planning before external beam radiation therapy, dosimetry is also needed to optimize PRRT to the individual patient. Accordingly, absorbed doses to organs at risk need to be calculated during PRRT, based on serial measurements of radioactivity distribution utilizing SPECT/CT. The dosimetry should be based on as few measurements as possible, while still retaining reliable results. The main aim of the present work was to calculate the fractional contribution of the extrapolations of the curve fits for the absorbed dose calculations to the kidneys. The secondary aim was to study agreement between absorbed dose (AD) and the effective half-life (t_eff) for the kidneys, estimated by means of measurements at one or two time points, in comparison to our current method employing three time points.

Methods

In 777 patients with disseminated neuroendocrine tumors undergoing PRRT, SPECT/CT over the abdomen was acquired at 1, 4, and 7 days after ¹⁷⁷Lu-DOTATATE infusion. The absorbed dose to the kidneys was calculated from SPECT/CT radioactivity distribution data, and the t_eff and fractional contributions of the extrapolations were estimated, utilizing data from one, two, and three time points, respectively.

Results

The fractional contributions from extrapolations before day 1 measurement and after day 7 measurement were approximately 26% and 11%, respectively. The mean differences in absorbed dose, based on one, two, and three time points were small, but with high method dependence for individual patients. The differences in estimated t_eff were small when it was based on measurements at days 1 and 7, but high for days 1 and 4 time points.

Conclusion

When assessing simplifications of methods for calculation of the absorbed dose to the kidneys, it was of the uttermost importance to incorporate the fractional contribution for the extrapolations included in the reference method. Measurements at an early and a late time point were found most important. An intermediate measurement contributes with an idea of the goodness of the fit.

A Rational Approach to Understanding and Evaluating Responsive Neurostimulation

Closed-loop brain stimulation is increasingly used in level 4 epilepsy centers without an understanding of how the device behaves on a daily basis. This lack of insight is a barrier to improving closed-loop therapy and ultimately understanding why some patients never achieve seizure reduction. We aimed to quantify the accuracy of closed-loop seizure detection and stimulation on the RNS device through extrapolating information derived from manually reviewed ECoG recordings and comprehensive device logging information. RNS System event logging data were obtained, reviewed, and analyzed using a custom-built software package. A weighted-means methodology was developed to adjust for bias and incompleteness in event logs and evaluated using Bland–Altman plots and Wilcoxon signed-rank tests to compare adjusted and non-weighted (standard method) results. Twelve patients implanted for a mean of 21.5 (interquartile range 13.5–31) months were reviewed. The mean seizure frequency reduction post-RNS implantation was 40.1% (interquartile range 0–96.2%). Three primary levels of event logging granularity were identified (ECoG recordings: 3.0% complete (interquartile range 0.3–1.8%); Event Lists: 72.9% complete (interquartile range 44.7–99.8%); Activity Logs: 100% complete; completeness measured with respect to Activity Logs). Bland–Altman interpretation confirmed non-equivalence with unpredictable differences in both magnitude and direction. Wilcoxon signed rank tests demonstrated significant (p < 10⁻⁶) differences in accuracy, sensitivity, and specificity at >5% absolute mean difference for extrapolated versus standard results. Device behavior logged by the RNS System should be used in conjunction with careful review of stored ECoG data to extrapolate metrics for detector performance and stimulation.

Economic evaluation and comparison of migration paths for the smart grid using two case studies

Today, European utility companies are facing the conversion of their power grids from a previously centrally controlled supply to a then decentralized supply. These changes are necessary to achieve the climate targets. In order to create a decentralized power grid, the integration of modern information and communication technologies (ICT) and other hardware is necessary. On the one hand, the utilities must know which paths they can take to make their power grid intelligent, but on the other hand it is also crucial to know the costs involved. In this contribution we outline a possible model for technological migration paths with a corresponding economic analysis based on German and European case studies.

Clinical and Regulatory Considerations for the Use of Bevacizumab Biosimilars in Metastatic Colorectal Cancer

Biosimilars - biological medicines highly similar to a licensed reference product (RP) - can mitigate the risk of drug shortages by providing treatment alternatives and, with their lower costs, increase patient access to medication and reduce health care expenditure. However, limited knowledge of biosimilar approval processes and lack of confidence in their quality and efficacy can limit their uptake. Importantly, biosimilars are approved based on tightly controlled regulatory pathways to demonstrate that the physical, chemical, and biological properties of the proposed biosimilar are highly similar to the RP, with no clinically meaningful differences. Initially, a battery of highly sensitive in vitro studies are performed, comparing critical quality attributes between the proposed biosimilar and RP. Subsequently, in vivo pharmacodynamic studies compare the activity and physiologic effects of the biosimilar and RP. Finally, clinical studies are conducted, including a pharmacokinetic equivalence study and a confirmatory comparative clinical trial. The latter is performed in the most sensitive patient population for which the RP is licensed, to provide the greatest possibility of identifying any clinically meaningful differences between the proposed biosimilar and RP. When equivalent safety and efficacy have been demonstrated in one setting, the totality of evidence, together with scientific justification that there are no anticipated differences between the RP and proposed biosimilar in mechanism of action, pharmacokinetics, immunogenicity or toxicity, allows extrapolation into indications where clinical studies were not performed with the proposed biosimilar. Here, we review the approval process for biosimilars, focusing on the licensing of bevacizumab biosimilars and their extrapolation to metastatic colorectal cancer.

L p bounds for rough parabolic maximal operators

In this article, we establish the Lp estimates for a certain class of rough parabolic maximal functions related to surfaces of revolution. The obtained estimates allow us to apply an extrapolation argument to extend and improve some previously known results.

Evaluation of survival extrapolation in immuno-oncology using multiple pre-planned data cuts: learnings to aid in model selection

BACKGROUND

Due to limited duration of follow up in clinical trials of cancer treatments, estimates of lifetime survival benefits are typically derived using statistical extrapolation methods. To justify the method used, a range of approaches have been proposed including statistical goodness-of-fit tests and comparing estimates against a previous data cut (i.e. interim data collected). In this study, we extend these approaches by presenting a range of extrapolations fitted to four pre-planned data cuts from the JAVELIN Merkel 200 (JM200) trial. By comparing different estimates of survival and goodness-of-fit as JM200 data mature, we undertook an iterative process of fitting and re-fitting survival models to retrospectively identify early indications of likely long-term survival.

METHODS

Standard and spline-based parametric models were fitted to overall survival data from each JM200 data cut. Goodness-of-fit was determined using an assessment of the estimated hazard function, information theory-based methods and objective comparisons of estimation accuracy. Best-fitting extrapolations were compared to establish which one provided the most accurate estimation, and how statistical goodness-of-fit differed.

RESULTS

Spline-based models provided the closest fit to the final JM200 data cut, though all extrapolation methods based on the earliest data cut underestimated the 'true' long-term survival (difference in restricted mean survival time [RMST] at 36 months: - 1.1 to - 0.5 months). Goodness-of-fit scores illustrated that an increasingly flexible model was favored as data matured. Given an early data cut, a more flexible model better aligned with clinical expectations could be reasonably justified using a range of metrics, including RMST and goodness-of-fit scores (which were typically within a 2-point range of the statistically 'best-fitting' model).

CONCLUSIONS

Survival estimates from the spline-based models are more aligned with clinical expectation and provided a better fit to the JM200 data, despite not exhibiting the definitively 'best' statistical goodness-of-fit. Longer-term data are required to further validate extrapolations, though this study illustrates the importance of clinical plausibility when selecting the most appropriate model. In addition, hazard-based plots and goodness-of-fit tests from multiple data cuts present useful approaches to identify when a more flexible model may be advantageous.

TRIAL REGISTRATION

JAVELIN Merkel 200 was registered with ClinicalTrials.gov as NCT02155647 on June 4, 2014.

Activity determination of 18F using liquid scintillation beta-efficiency extrapolation and non-extrapolation methods

The absolute activity of ¹⁸F was determined using 4πβ-γ liquid scintillation coincidence counting using beta-efficiency extrapolation. An ionization chamber factor was determined for use during a SIRTI comparison, for which results are presented. A non-extrapolation method based on a detection efficiency analysis was also employed using an adaptation of the double-phototube coincidence efficiency for a threshold above the second monopeak. Results and uncertainty budgets for the two methods are presented and discussed.

Extrapolation for a pharmacokinetic model for acetaminophen from adults to neonates: A Latin Hypercube Sampling analysis

Physiological and drug-specific parameters need to be adjusted when extrapolating a pharmacokinetic (PK) model from adults to neonates, so as to reproduce the time profiles of the studied drug(s) consistent with clinical, in vivo data or in vitro cell line measurements. In this paper we present a parameter analysis method, i.e. the Latin Hypercube Sampling (LHS) method for an acetaminophen (APAP) PK model. The original model consists of two compartments (the blood and the urine) with Michaelis-Menten kinetic parameters determined for APAP and its metabolites. The physiological parameters are scaled through allometric laws from adults to neonates, and APAP-specific parameters are adjusted for enzymatic maturational changes. The LHS method is used to statistically investigate the interplay between these parameters. The results for the extrapolated APAP model are consistent with published APAP PK data in neonates. We found the sulphation clearance parameter played a crucial role in the neonatal PK model, but its influence was weakened if the volume of distribution parameters were included. We suggest that this kind of in silico experiment could be valuable as the first step in PK model extrapolation between different ages.

Estimating intertidal seaweed biomass at larger scales from quadrat surveys

The amount of macroalgal biomass is an important ecosystem variable. Estimates can be made for a sampled area or values can be extrapolated to represent biomass over a larger region. Typically biomass is scaled-up using the area multiplied by the mean: a non-spatial method. Where algal biomass is patchy or shows gradients, non-spatial estimates for an area may be improved by spatial interpolation. A separate issue with scaling-up biomass estimates is that conventional confidence intervals based on the standard error (SE) of the sample may not be appropriate. The issues around interpolation and confidence intervals were examined for three fucoid species using data from 40 × 0.25 m-2 quadrats thrown in a 0.717 ha sampling plot on the shore of Galway Bay. Despite evidence of spatial autocorrelation, interpolation did not appear to improve estimates of the total plot biomass of Fucus serratus and F. vesiculosus. In contrast, interpolated estimates for Ascophyllum nodosum had less error than those based on the non-spatial method. Bootstrapped confidence intervals had several benefits over those based on the SE. These benefits include the avoidance of negative confidence limits at low sample sizes and no assumptions of normality in the data. If there is reason to expect strong patchiness or a gradient of biomass in the area of interest, interpolation is likely to produce more accurate estimates of biomass than non-spatial methods. Development of methodologies for biomass would benefit from more definition of local and regional gradients in biomass and their associated covariates.

HACSim: an R package to estimate intraspecific sample sizes for genetic diversity assessment using haplotype accumulation curves

Assessing levels of standing genetic variation within species requires a robust sampling for the purpose of accurate specimen identification using molecular techniques such as DNA barcoding; however, statistical estimators for what constitutes a robust sample are currently lacking. Moreover, such estimates are needed because most species are currently represented by only one or a few sequences in existing databases, which can safely be assumed to be undersampled. Unfortunately, sample sizes of 5-10 specimens per species typically seen in DNA barcoding studies are often insufficient to adequately capture within-species genetic diversity. Here, we introduce a novel iterative extrapolation simulation algorithm of haplotype accumulation curves, called HACSim (Haplotype Accumulation Curve Simulator) that can be employed to calculate likely sample sizes needed to observe the full range of DNA barcode haplotype variation that exists for a species. Using uniform haplotype and non-uniform haplotype frequency distributions, the notion of sampling sufficiency (the sample size at which sampling accuracy is maximized and above which no new sampling information is likely to be gained) can be gleaned. HACSim can be employed in two primary ways to estimate specimen sample sizes: (1) to simulate haplotype sampling in hypothetical species, and (2) to simulate haplotype sampling in real species mined from public reference sequence databases like the Barcode of Life Data Systems (BOLD) or GenBank for any genomic marker of interest. While our algorithm is globally convergent, runtime is heavily dependent on initial sample sizes and skewness of the corresponding haplotype frequency distribution.

Biosimilars in rheumatology

Biotechnologicals are an invaluable resource in the treatment of patients with inflammatory rheumatic diseases (IRD) non-responsive or intolerant to conventional therapies. However, they are the main driver for increase in direct costs and represent a significant economic burden to healthcare systems worldwide. Since biosimilars are similar and more affordable versions of previously licenced biotechnologicals, they are expected to contribute to healthcare system sustainability and reduce inequities in treatment access. The landmark approval of CT-P13 as the first infliximab biosimilar paved the way for new infliximab but also etanercept, adalimumab and rituximab biosimilars. In Europe, North America and some countries of Asia, development is strictly regulated and only those presenting a totality-of-evidence dossier with highly similar physicochemical, biological and clinical performances are endorsed by regulatory agencies as biosimilars. The current article addresses the importance of biosimilar medicines in the treatment of IRD, as well as their innovative development and regulatory pathways, clinical evidence of similarity and challenges that may undermine their widespread use and success.

Prevalence, incidence and future projection of diabetic eye disease in Europe: a systematic review and meta-analysis

To examine the prevalence and incidence of diabetic eye disease (DED) among individuals with diabetes in Europe, a systematic review to identify all published European prevalence and incidence studies of DED in individuals with diabetes managed in primary health care was performed according to the MOOSE and PRISMA guidelines. The databases Medline, Embase and Web of Science were searched to 2 September 2017. Meta-analyses and meta-regressions were performed. The pooled prevalence estimates were applied to diabetes prevalence rates provided by the International Diabetes Foundation atlas and Eurostat population data, and extrapolated to the year 2050. Data of 35 prevalence and four incidence studies were meta-analyzed. Any diabetic retinopathy (DR) and diabetic macular edema (DME) were prevalent in 25.7% (95% CI 22.8-28.8%) and 3.7% (95% CI 2.2-6.2%), respectively. In meta-regression, the prevalence of DR in persons with type 1 diabetes was significantly higher compared to persons with type 2 diabetes (54.4% vs. 25.0%). The pooled mean annual incidence of any DR and DME in in persons with type 2 diabetes was 4.6% (95% CI 2.3-8.8%) and 0.4% (95% CI 0.5-1.4%), respectively. We estimated that persons with diabetes affected by any DED in Europe will increase from 6.4 million today to 8.6 million in 2050, of whom 30% require close monitoring and/or treatment. DED is estimated to be present in more than a quarter of persons with type 2 diabetes and half of persons with type 1 diabetes underlining the importance of regular monitoring. Future health services need to be planned accordingly.

Survival Analysis in Patients with Metastatic Merkel Cell Carcinoma Treated with Avelumab

Introduction

Complex underlying risk functions associated with immuno-oncology treatments have led to exploration of different methods (parametric survival, spline, landmark, and cure-fraction models) to estimate long-term survival outcomes. The objective of this study was to examine differences in estimated short- and long-term survival in previously treated metastatic Merkel cell carcinoma (mMCC) patients receiving avelumab, when using alternative extrapolation approaches.

Methods

Efficacy data from the phase 2 JAVELIN Merkel 200 trial (part A) with at least 12 months of follow-up were analyzed. Standard parametric survival analyses and analyses of overall survival (OS) as a function of surrogate outcomes comprised of response (landmark analyses) and progression-free survival plus post-progression survival (PFS + PPS) were used to project OS. Overall survival throughout lifetime was projected and compared with the observed OS data with at least 24 months of follow-up.

Results

Estimated OS from all three approaches provided a good fit to the observed OS curve from at least 12 months of follow-up. However, performance compared with OS data from at least 24 months showed that the landmark approach followed by PFS + PPS provided a better fit to the data as compared to standard parametric analysis. Mean life expectancy estimated with avelumab was 2.48 years with best-fitting parametric function (a log-normal distribution), 3.15 years with the landmark approach, and 3.54 years with PFS + PPS.

Conclusion

Although standard parametric survival analysis may provide a good fit to short-term survival, it appears to underestimate the long-term survival benefits associated with avelumab in mMCC. Extrapolations based on surrogate outcomes of response or progression predict OS outcomes from longer follow-up better and appear to provide more clinically plausible projections.

Funding

EMD Serono Inc, Rockland, MA, a business of Merck KGaA, Darmstadt, Germany.

Electronic Supplementary Material

The online version of this article (10.1007/s12325-019-01034-0) contains supplementary material, which is available to authorized users.