The Economics of Pharmaceutical Development: Costs, Risks, and Incentives

This dissertation addresses three open questions related to the economics of pharmaceutical development. First, how much does it cost to conduct a clinical trial? Second, what effect has the model policy for incentivizing pharmaceutical development, the Orphan Drug Act, had on pharmaceutical availability? And third, how can the costs and risks of pharmaceutical development be used to model an optimal development portfolio? We estimate clinical trial costs by decomposing firms’ publicly reported research and development expenses against clinical trial data. We obtain estimates that are broadly consistent with older estimates based on proprietary data. We also estimate the costs of clinical trial subjects. To our knowledge, such costs have not been estimated previously. We find that the costs of Phase I and Phase II clinical trial subjects are very high, supporting the adoption of adaptive trial designs to decrease trial length and size. We measure the effects of the Orphan Drug Act by estimating the size of a regression discontinuity in drug prescriptions as a function of disease prevalence. We find no significant discontinuity around the prevalence threshold that qualifies products to receive “orphan incentives” under the Act. We offer a novel theoretical explanation for the lack of an observed discontinuity: the Act has a perverse effect on drug availability due to price effects of the orphan incentives. Last, we estimate the costs of the U.S. Public Health Emergency Medical Countermeasure Enterprise (PHEMCE), based on a survey of product pipelines, and design an optimal portfolio for achieving fixed success probabilities. Our results support the President’s budget request for PHEMCE but suggest that to achieve reasonable success probabilities, PHEMCE will need to prioritize some products over others, or reduce costs by funding smaller trials. We formally model the tradeoff of cost for safety, and describe some policy implications of the tradeoff

Machine Learning technique for isotopic determination of radioisotopes using HPGe γ\mathrm{\gamma}-ray spectra

$\mathrm{\gamma}$-ray spectroscopy is a quantitative, non-destructive technique that may be utilized for the identification and quantitative isotopic estimation of radionuclides. Traditional methods of isotopic determination have various challenges that contribute to statistical and systematic uncertainties in the estimated isotopics. Furthermore, these methods typically require numerous pre-processing steps, and have only been rigorously tested in laboratory settings with limited shielding. In this work, we examine the application of a number of machine learning based regression algorithms as alternatives to conventional approaches for analyzing $\mathrm{\gamma}$-ray spectroscopy data in the Emergency Response arena. This approach not only eliminates many steps in the analysis procedure, and therefore offers potential to reduce this source of systematic uncertainty, but is also shown to offer comparable performance to conventional approaches in the Emergency Response Application

A Phase I, First-in-Human Study of GSK2849330, an Anti-HER3 Monoclonal Antibody, in HER3-Expressing Solid Tumors

Background GSK2849330, an anti-HER3 monoclonal antibody that blocks HER3/Neuregulin 1 (NRG1) signaling in cancer cells, is engineered for enhanced antibody-dependent cellular cytotoxicity and complement-dependent cytotoxicity. This phase I, first-in-human, open-label study assessed the safety, pharmacokinetics (PK), pharmacodynamics, and preliminary activity of GSK2849330 in patients with HER3-expressing advanced solid tumors. Patients and Methods Patients with various tumor types were prospectively selected for HER3 expression by immunohistochemistry; a subset was also screened for NRG1 mRNA expression. In the dose-escalation phase, patients received GSK2849330 1.4-30 mg/kg every 2 weeks, or 3 mg/kg or 30 mg/kg weekly, intravenously (IV). In the dose-expansion phase, patients received 30 mg/kg GSK2849330 IV weekly. Results Twenty-nine patients with HER3-expressing cancers, of whom two expressed NRG1, received GSK2849330 (dose escalation: n = 18, dose expansion: n = 11). GSK2849330 was well tolerated. No dose-limiting toxicities were observed. The highest dose, of 30 mg/kg weekly, expected to provide full target engagement, was selected for dose expansion. Treatment-emergent adverse events (AEs) were mostly grade 1 or 2. The most common AEs were diarrhea (66%), fatigue (62%), and decreased appetite (31%). Dose-proportional plasma exposures were achieved, with evidence of HER3 inhibition in paired tissue biopsies. Of 29 patients, only 1 confirmed partial response, lasting 19 months, was noted in a patient with CD74-NRG1-rearranged non-small cell lung cancer (NSCLC). Conclusion GSK2849330 demonstrated a favorable safety profile, dose-proportional PK, and evidence of target engagement, but limited antitumor activity in HER3-expressing cancers. The exceptional response seen in a patient with CD74-NRG1-rearranged NSCLC suggests further exploration in NRG1-fusion-positive cancers. Implications for Practice This first-in-human study confirms that GSK2849330 is well tolerated. Importantly, across a variety of HER3-expressing advanced tumors, prospective selection by HER3/NRG1 expression alone was insufficient to identify patients who could benefit from treatment with this antibody-dependent cell-mediated cytotoxicity- and complement-dependent cytotoxicity-enhanced anti-HER3 antibody. The only confirmed durable response achieved was in a patient with CD74-NRG1-rearranged lung cancer. This highlights the potential utility of screening for NRG1 fusions prospectively across tumor types to enrich potential responders to anti-HER3 agents in ongoing trials

Serpentine Soils Do Not Limit Mycorrhizal Fungal Diversity

Background: Physiologically stressful environments tend to host depauperate and specialized biological communities. Serpentine soils exemplify this phenomenon by imposing well-known constraints on plants; however, their effect on other organisms is still poorly understood. Methodology/Principal Findings: We used a combination of field and molecular approaches to test the hypothesis that serpentine fungal communities are species-poor and specialized. We conducted surveys of ectomycorrhizal fungal diversity from adjacent serpentine and non-serpentine sites, described fungal communities using nrDNA Internal Transcribed Spacer (ITS) fragment and sequence analyses, and compared their phylogenetic community structure. Although we detected low fungal overlap across the two habitats, we found serpentine soils to support rich fungal communities that include representatives from all major fungal lineages. We failed to detect the phylogenetic signature of endemic clades that would result from specialization and adaptive radiation within this habitat. Conclusions/Significance: Our results indicate that serpentine soils do not constitute an extreme environment for ectomycorrhizal fungi, and raise important questions about the role of symbioses in edaphic tolerance and the maintenanc

Global transpiration data from sap flow measurements : the SAPFLUXNET database

Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land-atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The "sapfluxnetr" R package - designed to access, visualize, and process SAPFLUXNET data - is available from CRAN.Peer reviewe

The Economics of Pharmaceutical Development: Costs, Risks, and Incentives

This dissertation addresses three open questions related to the economics of pharmaceutical development. First, how much does it cost to conduct a clinical trial? Second, what effect has the model policy for incentivizing pharmaceutical development, the Orphan Drug Act, had on pharmaceutical availability? And third, how can the costs and risks of pharmaceutical development be used to model an optimal development portfolio? We estimate clinical trial costs by decomposing firms’ publicly reported research and development expenses against clinical trial data. We obtain estimates that are broadly consistent with older estimates based on proprietary data. We also estimate the costs of clinical trial subjects. To our knowledge, such costs have not been estimated previously. We find that the costs of Phase I and Phase II clinical trial subjects are very high, supporting the adoption of adaptive trial designs to decrease trial length and size. We measure the effects of the Orphan Drug Act by estimating the size of a regression discontinuity in drug prescriptions as a function of disease prevalence. We find no significant discontinuity around the prevalence threshold that qualifies products to receive “orphan incentives” under the Act. We offer a novel theoretical explanation for the lack of an observed discontinuity: the Act has a perverse effect on drug availability due to price effects of the orphan incentives. Last, we estimate the costs of the U.S. Public Health Emergency Medical Countermeasure Enterprise (PHEMCE), based on a survey of product pipelines, and design an optimal portfolio for achieving fixed success probabilities. Our results support the President’s budget request for PHEMCE but suggest that to achieve reasonable success probabilities, PHEMCE will need to prioritize some products over others, or reduce costs by funding smaller trials. We formally model the tradeoff of cost for safety, and describe some policy implications of the tradeoff