Going beyond probability of success: Opportunities for statisticians to influence quantitative decision-making at the portfolio level.

The pharmaceutical industry is plagued with long, costly development and high risk. Therefore, a company's effective management and optimisation of a portfolio of projects is critical for success. Project metrics such as the probability of success enable modelling of a company's pipeline accounting for the high uncertainty inherent within the industry. Making portfolio decisions inherently involves managing risk, and statisticians are ideally positioned to champion not only the derivation of metrics for individual projects, but also advocate decision-making at a broader portfolio level. This article aims to examine the existing different portfolio decision-making approaches and to suggest opportunities for statisticians to add value in terms of introducing probabilistic thinking, quantitative decision-making, and increasingly advanced methodologies.

Replenishing the pipeline: A quantitative approach to optimising the sourcing of new projects.

Large pharmaceutical companies maintain a portfolio of assets, some of which are projects under development while others are on the market and generating revenue. The budget allocated to R&D may not always be sufficient to fund all the available projects for development. Much attention has been paid to the selection of optimal subsets of available projects to fit within the available budget. In this paper, we argue the need for a forward-looking approach to portfolio decision-making. We develop a quantitative model that allows the portfolio management to evaluate the need for future inflow of new projects to achieve revenue at desired levels, often aspiring to a certain annual revenue growth. Optimisation methods are developed for the presented model, allowing an optimal choice of number, timing and type of projects to be added to the portfolio. The proposed methodology allows for a proactive approach to portfolio management, prioritisation, and optimisation. It provides a quantitatively based support for strategic decisions regarding the efforts needed to secure the future development pipeline and revenue stream of the company.

Quantifying the Benefits of Digital Biomarkers and Technology-Based Study Endpoints in Clinical Trials: Project Moneyball.

Introduction: Digital biomarkers have significant potential to transform drug development, but only a few have contributed meaningfully to bring new treatments to market. There are uncertainties in how they will generate quantifiable benefits in clinical trial performance and ultimately to the chances of phase 3 success. Here we have proposed a statistical framework and ran a proof-of-concept model with hypothetical digital biomarkers and visualized them in a familiar manner to study power calculation. Methods: A Monte Carlo simulation for Parkinson's disease (PD) was performed using the Captario SUM® platform and illustrative study technology impact calculations were generated. We took inspiration from the EMA-qualified wearable-derived digital endpoint stride velocity 95th centile (SV95C) for Duchenne muscular dystrophy, and we imagined a similar measurement for PD would be available in the future. DaTscan enrichment and "SV95C-like" endpoint biomarkers were assumed on a hypothetical disease-modifying drug pivotal trial aiming for an 80% probability of achieving a study p value of less than 0.05. Results: Four scenarios with different combinations of technologies were illustrated. The model illustrated a way to quantify the magnitude of the contributions that enrichment and endpoint technologies could make to drug development studies. Discussion/Conclusion: Quantitative models could be valuable not only for the study sponsors but also as an interactive and collaborative engagement tool for technology players and multi-stakeholder consortia. Establishing values of digital biomarkers could also facilitate business cases and financial investments.

Do strict decision criteria hamper productivity in the pharmaceutical industry?

The discouragingly high rates of attrition in drug development, and in particular in Phase 2, warrant a closer look at the decision criteria applied for investment in the next phase (Phase 3). We have in this article evaluated Stop/Go criteria after Phase 2, based on a model encompassing both Phase 2 and 3, as well as the eventual outcome on the market. The results indicate that the value of a drug project is often maximized if rather liberal decision criteria are applied. The routine adherence to standard criteria, e.g. requiring significance at 5% level, may lead to an unduly high rate of false negative decisions. This might ultimately hamper the productivity of drug development and leading to potentially useful drugs not being taken forward to benefit the intended patients.

Selection bias, investment decisions and treatment effect distributions.

When making decisions regarding the investment and design for a Phase 3 programme in the development of a new drug, the results from preceding Phase 2 trials are an important source of information. However, only projects in which the Phase 2 results show promising treatment effects will typically be considered for a Phase 3 investment decision. This implies that, for those projects where Phase 3 is pursued, the underlying Phase 2 estimates are subject to selection bias. We will in this article investigate the nature of this selection bias based on a selection of distributions for the treatment effect. We illustrate some properties of Bayesian estimates, providing shrinkage of the Phase 2 estimate to counteract the selection bias. We further give some empirical guidance regarding the choice of prior distribution and comment on the consequences for decision-making in investment and planning for Phase 3 programmes.

A modelling framework for improved design and decision-making in drug development.

The development of a new drug is an extremely high-risk enterprise. The attrition rates of development projects and the average costs for each launched product are daunting, and the completion of a development program requires a very long time horizon. These facts imply that there are huge potential gains, should one be able to improve efficiency and enhance decision-making capabilities. In this paper, we argue that substantial gains can be achieved by adapting a holistic view of drug development. Historically, too much planning, design and decision-making in the pharmaceutical development has been based on locally optimising separate parts of the development program, and too often important sources of uncertainty are ignored. We propose instead a model-based approach built on two essential pillars; (1) an integrated holistic view of the development program, including post-launch marketing and sales, with all parts evaluated simultaneously; (2) an explicit appreciation of all relevant sources of uncertainty. Computer simulations are utilised to evaluate the properties of the program options at hand, and to provide valuable quantitative decision support. Applications of this modelling approach have proven to add large value to development projects in terms of better program options being generated and more value-adding decisions taken.

Pharmacological inhibition of the chemokine receptor CX3CR1 attenuates disease in a chronic-relapsing rat model for multiple sclerosis.

One hallmark of multiple sclerosis (MS) and experimental autoimmune encephalomyelitis (EAE) is infiltration of leukocytes into the CNS, where chemokines and their receptors play a major mediatory role. CX3CR1 is a chemokine receptor involved in leukocyte adhesion and migration and hence a mediator of immune defense reactions. The role of CX3CR1 in MS and EAE pathogenesis however remains to be fully assessed. Here, we demonstrate CX3CR1 mRNA expression on inflammatory cells within active plaque areas in MS brain autopsies. To test whether blocking CNS infiltration of peripheral leukocytes expressing CX3CR1 would be a suitable treatment strategy for MS, we developed a selective, high-affinity inhibitor of CX3CR1 (AZD8797). The compound is active outside the CNS and AZD8797 treatment in Dark Agouti rats with myelin oligodendrocyte glycoprotein-induced EAE resulted in reduced paralysis, CNS pathology, and incidence of relapses. The compound is effective when starting treatment before onset, as well as after the acute phase. This treatment strategy is mechanistically similar to, but more restricted than, current very late antigen-4-directed approaches that have significant side effects. We suggest that blocking CX3CR1 on leukocytes outside the CNS could be an alternative approach to treat MS.

Modelling and simulation in the pharmaceutical industry--some reflections.

Modelling and simulation (M&S) is increasingly being applied in (clinical) drug development. It provides an opportune area for the community of pharmaceutical statisticians to pursue. In this article, we highlight useful principles behind the application of M&S. We claim that M&S should be focussed on decisions, tailored to its purpose and based in applied sciences, not relying entirely on data-driven statistical analysis. Further, M&S should be a continuous process making use of diverse information sources and applying Bayesian and frequentist methodology, as appropriate. In addition to forming a basis for analysing decision options, M&S provides a framework that can facilitate communication between stakeholders. Besides the discussion on modelling philosophy, we also describe how standard simulation practice can be ineffective and how simulation efficiency can often be greatly improved.

A novel approach to data processing of the QT interval response in the conscious telemetered beagle dog.

INTRODUCTION: Drug-induced QT interval prolongation may lead to ventricular arrhythmias. The aim of the study was to optimize QT interval data processing to quantify drug-induced QT interval prolongation in the telemetry instrumented conscious dog model. METHODS: The test substances cisapride, dofetilide, haloperidol, and terfenadine and corresponding vehicles were given to male and female beagle dogs during two consecutive 90-min intravenous infusions. Cardiovascular parameters were recorded for 24 h and exposure to the drugs was measured. The delayed response in the QT interval after an abrupt change in heart rate was investigated. Eight mathematical models to describe the QT interval-heart rate relationship were compared and different sets of covariates were used to quantify the drug-induced effect on the QT interval. RESULTS: After an abrupt decrease in heart rate, a 75% adaptation of the QT interval was reached after 54+/-9 s. A linear model was preferred to correct the drug-induced effect on the QT interval for heart rate, vehicle effect, serial correlation, plasma concentration and time of day. All test substances significantly prolonged the QT interval. DISCUSSION: To optimize the processing of QT interval data, the delay in QT interval response after an abrupt change in heart rate should be considered. The QT interval-heart rate relationship and vehicle response were individual-specific and corrections were therefore made individually. When estimating the drug-induced effect on the QT interval it is considered advantageous to use plasma concentration as a covariate, as well as adjusting for vehicle effect and serial correlation in measurements. The conscious dog model detected significant increases in the QT interval for all test substances investigated.

Pharmacokinetic-pharmacodynamic modeling of drug-induced effect on the QT interval in conscious telemetered dogs.

INTRODUCTION: To assure drug safety, the investigation of the relationship between plasma concentration and drug-induced prolongation of the QT interval of the ECG is a challenge in drug discovery. For this purpose, dofetilide was utilized to demonstrate the benefits of characterizing the complete time course of concentrations and effect in conscious beagle dogs in the assessment of drug safety. METHOD: On two separate occasions, four male and two female beagle dogs were given vehicle or the test substance, dofetilide (0.25 mumol/kg), over a 3-h intravenous infusion. Cardiovascular parameters, including QT intervals, were recorded for 24-h using radiotelemetry. The QT interval was corrected individually for heart rate, vehicle treatment, and serial correlation (QT(c)). Exposure (plasma concentration) to dofetilide was measured and described by a two-compartment model. The individual concentration-time course of dofetilide was linked to the QT(c) interval via an effect compartment and a pharmacodynamic E(max) model, to account for the observed hysteresis. RESULTS: Dofetilide induced a concentration-dependent increase in the QT(c) interval, with an EC(50) of 9 nM (3-30 nM, 95% C.I.) and an E(max) of 59+/-9 ms. A hysteresis loop was observed by plotting plasma concentrations vs. QT interval in time order, indicating a delay in onset of effect. It was found to have an equilibrium half-life of 11+/-8 min. Based on the parameters potency and E(max), a representation was made of the drug-induced changes to the QT interval. DISCUSSION: An effect compartment model was found to accurately mimic the QT interval prolongation following administration of the test substance, dofetilide. The assessment of the individual concentration-effect relationship and confounding factors such as hysteresis might provide a better prediction of the safety profiles of new drug candidates.

Avoidance response of sediment living amphipods to zinc pyrithione as a measure of sediment toxicity.

An avoidance test was developed using non-cultured individuals of the sediment dwelling amphipod Monoporeia affinis. As test substance we used zinc pyrithione, an antifouling agent and a common shampoo ingredient. The toxicity to Daphnia and fish is well known but sediment toxicity of this very hydrophobic compound is less known. The preference of juvenile M. affinis was tested in jars, each including 12 petri dishes. In each replicate, half of the petri dishes contained sediment mixed with six concentrations ranging from 0 to 10microg zinc pyrithione per L sediment and half of the petri dishes contained the corresponding sediment-substance mixture plus an extra food addition. The amphipods significantly avoided petri dishes with the three highest concentrations of zinc pyrithione and the calculated EC(50) was 9.65microgL(-1) sediment. No difference in mortality was observed between concentrations. Using the avoidance behaviour in sediment toxicity testing is a simple and cost-effective screening for toxicants.

Benefits of combining the sexes when evaluating data from toxicological studies.

In the assessment of the safety of a new drug, a large battery of toxicological studies is performed. In toxicological studies it is common practice to analyse the data from the sexes separately. We argue that this is not best practice and that much is to be gained from combining data from both sexes when evaluating studies. The main benefits and arguments in favour of combining the data are: (i) Improved efficiency, allowing fewer test animals and/or better precision. (ii) Other phases of drug development, clinical trials in particular, combine sexes. (iii) Most substances act similarly on both sexes and most drugs are prescribed without sex differentiation. (iv) If sex differences are of interest, combined data facilitates the quantification of the difference. (v) Reduced number of false positive and false negative findings. Although there might be some grounds to analysing the sexes separately, we argue that these are comparatively minor. We do not promote simply pooling the data and neglecting the existence of two sexes when comparing treatment groups. Rather, the analysis of combined data should allow for sex differences, for instance by using stratified procedures or by including a sex factor in a statistical model. We illustrate from real study data where the approach we propose has substantial benefits over traditional methods. A theoretical illustration is provided to quantify mathematically the potential gains and the corresponding sample size reduction, i.e. a reduction of animal use of ca. 50%, that would be possible without impairing the precision of studies.

Dynamics of lipids and polychlorinated biphenyls in a Baltic amphipod (Monoporeia affinis): a field study.

Processes such as accumulation and elimination, which control tissue concentration of polychlorinated biphenyls (PCBs), were examined over time in an in situ study of the amphipod Monoporeia affinis. These processes were studied with respect to individual PCB congeners, percentage lipid and composition, and body weight. A secondary objective was to examine the impact of seasonal variability in percentage lipids and lipid composition on PCB concentration in two coexisting Baltic amphipods, M. affinis and Pontoporeia femorata. Polycholorinated biphenyl concentrations tended to be higher in P. femorata than in M. affinis, possibly because of P. femorata's lower respiration rate and larger size. The net accumulation of PCBs was congener dependent and negatively correlated to lipid concentration. The relation between the net concentration change rate of 16 PCB congeners over time and log Kow was not significant during the spring and summer months, a time when lipid accumulation and strong growth occur. In contrast, the net concentration change rate of the corresponding PCB congeners over time during autumn and early winter, that is, from the period before gonad maturation to the period after mating and early embryogenesis, showed a significant relation to tog Kow (r2 = 0.62, p < 0.001, n = 16). During the latter period, amphipod lipid weight was reduced while the PCB body burden increased. Results strongly indicate that elimination rather than accumulation is the main process controlling amphipod body burden. This pattern results in a transfer of PCBs from the female to the developing embryos, which is reflected in high PCB body burden in newly released offspring.

Aspects of design and statistical analysis in the Comet assay.

Some aspects of the statistical design and analysis of the Comet (single cell gel electrophoresis) assay have been evaluated by means of a simulation study. The tail length and tail moment were selected for the quantification of DNA migration. Results from the simulation study showed that the choice of measure to summarize the cells on each slide is extremely important in order to facilitate an efficient analysis. For tail moment, the mean of log transformed data is clearly superior to the other evaluated measures, whereas using the mean of raw data without transformation can lead to very inefficient analyses. The 90th percentile, capturing the upper tail of the distribution, performs well for the tail length, with a slight improvement obtained by applying a log transformation prior to calculations. Furthermore, the simulation study has been used to assess the appropriateness of some models for statistical analysis and to address the issue of design (i.e. number of cultures or animals in each group, number of slides per animal/culture and number of cells scored per slide). Combining the results from the simulations with practical experience from the pharmaceutical industry, we conclude the paper by providing concise recommendations regarding the design and statistical analysis in the Comet assay.