The pharmaceutical productivity gap - Incremental decline in R&D efficiency despite transient improvements.

Rising research and development costs, currently exceeding $3.5 billion per novel drug, reflect a five-decade decline in pharmaceutical R&D efficiency. While recent reports suggest a potential turnaround, this review offers a systems-level analysis to explore whether this marks a structural shift or transient reversal. We analyzed financial data from the 200 largest pharmaceutical firms, novel drug approvals, and more than 80 000 clinical trials between 2012 and 2023. Our analysis revealed that despite recent stabilization, the pharmaceutical industry continues to face challenges, particularly due to elevated late-stage clinical attrition, suggesting that a sustained turnaround in R&D efficiency remains elusive.

Design and modeling of order of addition experiment with component effects.

An order of addition experiment is an experiment to study how the order of addition of components affect the results, with the objective of predicting and determining the optimal order of addition of components. Order of addition experiment are also commonly used in the drug combination therapy, where experimenting with all drugs combinations is unaffordable. To solve this problem, we constructed a new design table, two-level component factorial design table (TLCF), which combine the component orthogonal array design table and the two-level partial factorial design table by matrix product. TLCF can explore the order and dosage effect of components on the results and can greatly reduce the number of experiments. We also prove that the relative D-efficiency of the TLCF can reach 100% and solve an explicit expression for the D-efficiency of the full design. In the simulation experiment, we compare the D-efficiency of the TLCF with the random design table to prove the superiority of TLCF. Finally, we give a treatment plan for the combination of three drugs for glioblastoma based on the TLCF, which provides a new perspective for the precision treatment of patients.

Efficient Study Design and Analysis of Longitudinal Dose-Response Data Using Fractional Polynomials.

Correctly characterising the dose-response relationship and taking the correct dose forward for further study is a critical part of the drug development process. We use optimal design theory to compare different designs and show that using longitudinal data from all available timepoints in a continuous-time dose-response model can substantially increase the efficiency of estimation of the dose-response compared to a single timepoint model. We give theoretical results to calculate the efficiency gains for a large class of these models. For example, a linearly growing Emax dose-response in a population with a between/within-patient variance ratio ranging from 0.1 to 1 measured at six visits can be estimated with between 1.43 and 2.22 times relative efficiency gain, or equivalently, with 30% to a 55% reduced sample size, compared to a single model of the final timepoint. Fractional polynomials are a flexible way to incorporate data from repeated measurements, increasing precision without imposing strong constraints. Longitudinal dose-response models using two fractional polynomial terms are robust to mis-specification of the true longitudinal process while maintaining, often large, efficiency gains. These models have applications for characterising the dose-response at interim or final analyses.

Data-driven analysis on the performance evaluation of national R&D projects in Korea.

Research and development (R&D) is a crucial competency in both developing and developed countries. As a result, evaluating the performance of R&D programs has become a significant research topic for academic and governmental researchers. This study aims to investigate the impact of various factors, such as the characteristics of national R&D projects, research stages, technology types, and management institutions, on their performance. Specifically, we focus on identifying key factors that influence the efficiency of national R&D investments in South Korea. To achieve this, we compiled a dataset of 98,224 government-funded R&D projects conducted between 2016 and 2019. The dataset includes information on project characteristics (research stage, technology types, and management institutions) as well as outcomes (patent applications, patent registrations, publications, royalties, and sales). Through factorial Kruskal-Wallis tests, we found that the research stage and technology type significantly affected the project outcomes, while the research stage did not significantly influence royalty and sales amounts. Additionally, our analysis of South Korean research management institutions revealed variations in their overall performance, suggesting differences in management capabilities among institutions. Based on these findings, we provide insights into setting appropriate research goals for each project, considering their unique characteristics. Finally, we discuss the implications and limitations of this study.

The Evolution of Flow Chemistry: An Opinion on Factors Driving Innovation.

This article seeks to provide an overview of the environmental factors within the pharmaceutical industry that have contributed to the emergence of flow chemistry over the past two decades. It highlights some of the challenges facing the industry and describes how they are being overcome by the exponential trajectory of scientific progress in the area. We identify current trends and offer a speculative glimpse into the future of drug development and manufacturing with some examples of progress being made at CARBOGEN AMCIS.

The impacts of driving variables on energy-related carbon emissions reduction in the building sector based on an extended LMDI model: a case study in China.

As China's main contributor to energy-related carbon emissions, the building sector in Jiangsu Province generates around 13.58% of the national carbon emissions. However, the influential variables of the energy structure in Jiangsu Province have been little investigated during the past decade. With the increasing emphasis on China's investment in technological innovation and adjustment of its industrial structure, research and development (R&D) has become an inevitable area for carbon emissions reduction. Nevertheless, its role in carbon emissions has rarely been examined. In this research, based on the logarithmic mean Divisia index (LMDI) model, the variables affecting the fluctuation of carbon dioxide emissions in the building sector (CEBS) in Jiangsu Province during 2011-2019 were restructured by introducing technological factors related to the construction industry, including energy structure, energy intensity, R&D efficiency, R&D intensity, investment intensity, economic output, and population engaged in the construction industry. From the results, it can be inferred that (1) energy structure, energy intensity, R&D efficiency, and investment intensity operate as inhibitors in increasing CEBS, and investment intensity exerts a more prominent impact on suppressing the growth of CEBS; (2) R&D intensity, economic output, and population engaged have a promotional effect on the fluctuations of CEBS, among which the first factor most actively promoted the increase in carbon emissions, although its role was negligible for economic output and the population; and (3) R&D efficiency, R&D intensity, and investment intensity are the three most critical variables for influencing the CEBS, but they are volatile. The numerical fluctuation caused by the three factors might be correlated to national and local policy interventions. Finally, policy recommendations are put forward for strengthening the management and minimizing the CEBS in Jiangsu Province.

Analysis of pharma R&D productivity - a new perspective needed.

R&D productivity continues to be the industry's grand challenge. We analyzed the R&D input, output, and outcome of 16 leading research-based pharmaceutical companies over 20 years (2001-2020). Our analysis shows that pharma companies increased their R&D spending at a compound annual growth rate of 6% (2001-2020) to an average R&D expenditure per company of $6.7 billion (2020). The companies in our investigation launched 251 new drugs representing 46% of all CDER-related FDA approvals in the past 20 years. The average R&D efficiency of big pharma was $6.16 billion total R&D expenditures per new drug. Almost half of the leading companies needed to compensate for their negative R&D productivity through mergers and acquisitions.

Impact of the Error Structure on the Design and Analysis of Enzyme Kinetic Models.

The statistical analysis of enzyme kinetic reactions usually involves models of the response functions which are well defined on the basis of Michaelis-Menten type equations. The error structure, however, is often without good reason assumed as additive Gaussian noise. This simple assumption may lead to undesired properties of the analysis, particularly when simulations are involved and consequently negative simulated reaction rates may occur. In this study, we investigate the effect of assuming multiplicative log normal errors instead. While there is typically little impact on the estimates, the experimental designs and their efficiencies are decisively affected, particularly when it comes to model discrimination problems.

The role of DMPK science in improving pharmaceutical research and development efficiency.

The successful regulatory authority approval rate of drug candidates in the drug development pipeline is crucial for determining pharmaceutical research and development (R&D) efficiency. Regulatory authorities include the US Food and Drug Administration (FDA), European Medicines Agency (EMA), and Pharmaceutical and Food Safety Bureau Japan (PFSB), among others. Optimal drug metabolism and pharmacokinetics (DMPK) properties influence the progression of a drug candidate from the preclinical to the clinical phase. In this review, we provide a comprehensive assessment of essential concepts, methods, improvements, and challenges in DMPK science and its significance in drug development. This information provides insights into the association of DMPK science with pharmaceutical R&D efficiency.

R&D efficiency of leading pharmaceutical companies - A 20-year analysis.

Comparative analysis of the R&D efficiency of 14 leading pharmaceutical companies for the years 1999-2018 shows that there is a close positive correlation between R&D spending and the two investigated R&D output parameters, approved NMEs and the cumulative impact factor of their publications. In other words, higher R&D investments (input) were associated with higher R&D output. Second, our analyses indicate that there are 'economies of scale' (size) in pharmaceutical R&D.

Designs for order-of-addition experiments.

The order-of-addition experiment aims at determining the optimal order of adding components such that the response of interest is optimized. Order of addition has been widely involved in many areas, including bio-chemistry, food science, nutritional science, pharmaceutical science, etc. However, such an important study is rather primitive in statistical literature. In this paper, a thorough study on pair-wise ordering designs for order of addition is provided. The recursive relation between two successive full pair-wise ordering designs is developed. Based on this recursive relation, the full pair-wise ordering design can be obtained without evaluating all the orders of components. The value of the D-efficiency for the full pair-wise ordering model is then derived. It provides a benchmark for choosing the fractional pair-wise ordering designs. To overcome the unaffordability of the full pair-wise ordering design, a new class of minimal-point pair-wise ordering designs is proposed. A job scheduling problem as well as simulation studies are conducted to illustrate the performance of the pair-wise ordering designs for determining the optimal orders. It is shown that the proposed designs are very efficient in determining the optimal order of addition.

A new randomization procedure based on multiple covariates and applicable to parallel studies with simultaneous enrollment of all subjects prior to intervention.

BACKGROUND: Parallel intervention studies involving volunteers usually require a procedure to allocate the subjects to study-arms. Statistical models to evaluate the different outcomes of the study-arms will include study-arm as a factor along with any covariate that might affect the results. To ensure that the effects of the covariates are confounded to the least possible extent with the effects of the arms, stratified randomization can be applied. However, there is at present no clear-cut procedure when there are multiple covariates. METHODS: For parallel study designs with simultaneous enrollment of all subjects prior to intervention, we propose a D-optimal blocking procedure to allocate subjects with known values of the covariates to the study arms. We prove that the procedure minimizes the variances of the baseline differences between the arms corrected for the covariates. The procedure uses standard statistical software. RESULTS: We demonstrate the potential of the method by an application to a human parallel nutritional intervention trial with three arms and 162 healthy volunteers. The covariates were gender, age, body mass index, an initial composite health score, and a categorical indicator called first-visit group, defining groups of volunteers who visit the clinical centre on the same day (17 groups). Volunteers were allocated equally to the study-arms by the D-optimal blocking procedure. The D-efficiency of the model connecting an outcome with the study-arms and correcting for the covariates equals 99.2%. We simulated 10,000 random allocations of subjects to arms either unstratified or stratified by first-visit group. Intervals covering the middle 95% of the D-efficiencies for these allocations were [82.0, 92.0] and [93.2, 98.4], respectively. CONCLUSIONS: Allocation of volunteers to study-arms with a D-optimal blocking procedure with the values of the covariates as inputs substantially improves the efficiency of the statistical model that connects the response with the study arms and corrects for the covariates. TRIAL REGISTRATION: Dutch Trial Register NL7054 ( NTR7259 ). Registered May 15, 2018.

Industrial Energy-Related CO2 Emissions and Their Driving Factors in the Yangtze River Economic Zone (China): An Extended LMDI Analysis from 2008 to 2016.

As the world's largest developing country in the world, China consumes a large amount of fossil fuels and this leads to a significant increase in industrial energy-related CO2 emissions (IECEs). The Yangtze River Economic Zone (YREZ), accounting for 21.4% of the total area of China, generates more than 40% of the total national gross domestic product and is an important component of the IECEs from China. However, little is known about the changes in the IECEs and their influencing factors in this area during the past decade. In this study, IECEs were calculated and their influencing factors were delineated based on an extended logarithmic mean Divisia index (LMDI) model by introducing technological factors in the YREZ during 2008-2016. The following conclusions could be drawn from the results. (1) Jiangsu and Hubei were the leading and the second largest IECEs emitters, respectively. The contribution of the cumulative increment of IECEs was the strongest in Jiangsu, followed by Anhui, Jiangxi and Hunan. (2) On the whole, both the energy intensity and R&D efficiency play a dominant role in suppressing IECEs; the economic output and investment intensity exert the most prominent effect on promoting IECEs, while there were great differences among the major driving factors in sub-regions. Energy structure, industrial structure and R&D intensity play less important roles in the IECEs, especially in the central and western regions. (3) The year of 2012 was an important turning point when nearly half of these provinces showed a change in the increment of IECEs from positive to negative values, which was jointly caused by weakening economic activity and reinforced inhibitory of energy intensity and R&D intensity.

Current and Future Challenges in Modern Drug Discovery.

Drug discovery is an expensive, time-consuming, and risky business. To avoid late-stage failure, learnings from past projects and the development of new approaches are crucial. New modalities and emerging new target spaces allow the exploration of unprecedented indications or to address so far undrugable targets. Late-stage attrition is usually attributed to the lack of efficacy or to compound-related safety issues. Efficacy has been shown to be related to a strong genetic link to human disease, a better understanding of the target biology, and the availability of biomarkers to bridge from animals to humans. Compound safety can be improved by ligand optimization, which is becoming increasingly demanding for difficult targets. Therefore, new strategies include the design of allosteric ligands, covalent binders, and other modalities. Design methods currently heavily rely on artificial intelligence and advanced computational methods such as free energy calculations and quantum chemistry. Especially for quantum chemical methods, a more detailed overview is given in this chapter.

Investigating the impact of design characteristics on statistical efficiency within discrete choice experiments: A systematic survey.

OBJECTIVES: This study reviews simulation studies of discrete choice experiments to determine (i) how survey design features affect statistical efficiency, (ii) and to appraise their reporting quality. OUTCOMES: Statistical efficiency was measured using relative design (D-) efficiency, D-optimality, or D-error. METHODS: For this systematic survey, we searched Journal Storage (JSTOR), Since Direct, PubMed, and OVID which included a search within EMBASE. Searches were conducted up to year 2016 for simulation studies investigating the impact of DCE design features on statistical efficiency. Studies were screened and data were extracted independently and in duplicate. Results for each included study were summarized by design characteristic. Previously developed criteria for reporting quality of simulation studies were also adapted and applied to each included study. RESULTS: Of 371 potentially relevant studies, 9 were found to be eligible, with several varying in study objectives. Statistical efficiency improved when increasing the number of choice tasks or alternatives; decreasing the number of attributes, attribute levels; using an unrestricted continuous "manipulator" attribute; using model-based approaches with covariates incorporating response behaviour; using sampling approaches that incorporate previous knowledge of response behaviour; incorporating heterogeneity in a model-based design; correctly specifying Bayesian priors; minimizing parameter prior variances; and using an appropriate method to create the DCE design for the research question. The simulation studies performed well in terms of reporting quality. Improvement is needed in regards to clearly specifying study objectives, number of failures, random number generators, starting seeds, and the software used. CONCLUSION: These results identify the best approaches to structure a DCE. An investigator can manipulate design characteristics to help reduce response burden and increase statistical efficiency. Since studies varied in their objectives, conclusions were made on several design characteristics, however, the validity of each conclusion was limited. Further research should be conducted to explore all conclusions in various design settings and scenarios. Additional reviews to explore other statistical efficiency outcomes and databases can also be performed to enhance the conclusions identified from this review.

A web-based tool for designing experimental studies to detect hormesis and estimate the threshold dose.

Hormesis has been widely observed and debated in a variety of context in biomedicine and toxicological sciences. Detecting its presence can be an important problem with wide ranging implications. However, there is little work on constructing an efficient experiment to detect its existence or estimate the threshold dose. We use optimal design theory to develop a variety of locally optimal designs to detect hormesis, estimate the threshold dose and the zero-equivalent point (ZEP) for commonly used models in toxicology and risk assessment. To facilitate use of more efficient designs to detect hormesis, estimate threshold dose and estimate the ZEP in practice, we implement computer algorithms and create a user-friendly web site to help the biomedical researcher generate different types of optimal designs. The online tool facilitates the user to evaluate robustness properties of a selected design to various model assumptions and compare designs before implementation.

Economic analysis of opportunities to accelerate Alzheimer's disease research and development.

The development of disease-modifying treatments for Alzheimer's disease (AD) faces a number of barriers. Among these are the lack of surrogate biomarkers, the exceptional size and duration of clinical trials, difficulties in identifying appropriate populations for clinical trials, and the limitations of monotherapies in addressing such a complex multifactorial disease. This study sets out to first estimate the consequent impact on the expected cost of developing disease-modifying treatments for AD and then to estimate the potential benefits of bringing together industry, academic, and government stakeholders to co-invest in, for example, developing better biomarkers and cognitive assessment tools, building out advanced registries and clinical trial-readiness cohorts, and establishing clinical trial platforms to investigate combinations of candidate drugs and biomarkers from the portfolios of multiple companies. Estimates based on interviews with experts on AD research and development suggest that the cost of one new drug is now $5.7 billion (95% confidence interval (CI) $3.7-9.5 billion) and could be reduced to $2.0 billion (95% CI $1.5-2.9 billion). The associated acceleration in the arrival of disease-modifying treatments could reduce the number of case years of dementia by 7.0 million (95% CI 4.4-9.4 million) in the United States from 2025 through 2040.

Research and development productivity map: visualization of industry status.

WHAT IS KNOWN AND OBJECTIVE: Decline in research and development (R&D) productivity and changes in the business environment have led to pharmaceutical company management to strive to improve R&D productivity. This decline is widely considered to be a major cause of industry consolidation and has received increased scholarly attention. This study aims to construct an R&D productivity map to visualize the industry's R&D productivity and to identify similarity in corporate actions with a view to investigate whether there is a relationship between deterioration in R&D productivity and industry consolidation. METHODS: Research and development productivity is decomposed into two subprocesses to measure productivity: R&D efficiency and R&D effectiveness, and scores were calculated using a two-stage data envelopment analysis (DEA). The map is then constructed by projecting outputs. To identify any relationship between DEA scores and merger and acquisition transactions, a multiple regression model is employed. RESULTS AND DISCUSSION: Data on 21 global pharmaceutical companies, statistical results indicated that companies with lower R&D efficiency scores were more likely to engage in consolidation. Three US companies that were least successful in terms of R&D effectiveness, as measured by our indicators, were either acquired or changed their business model. CONCLUSION: The R&D productivity map is a useful means for visualizing productivity among companies. By grouping companies into four groups, behavioural commonalities can be observed. The R&D productivity map should be useful for monitor the industry's productivity and help to improve it.

Pharmacogenomic Innovative Solutions.

Pharmacogenomic Innovative Solutions is a new company of pharmacogenomics experts. It uses its expertise in pharmacogenomics to enhance the efficiency of drug development, to make more drugs available for patients, and to increase the clinical effectiveness of drugs, in order to ensure patients receive better treatment. To do this, the company has two parts to its business strategy: consultancy to pharmaceutical and biotechnology clients to optimize development pipelines for drug launch; and partnering with drug-discovery organizations to increase its portfolio through in-licensing or codevelopment.