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Huntington's disease (HD) is an autosomal-dominant inherited neurodegenerative disorder that is caused by expansion of a CAG-repeat tract in the huntingtin gene and characterized by motor impairment, cognitive decline, and neuropsychiatric disturbances. Neuropathological studies show that disease progression follows a characteristic pattern of brain atrophy, beginning in the basal ganglia structures. The HD Regulatory Science Consortium (HD-RSC) brings together diverse stakeholders in the HD community—biopharmaceutical industry, academia, nonprofit, and patient advocacy organizations—to define and address regulatory needs to accelerate HD therapeutic development. Here, the Biomarker Working Group of the HD-RSC summarizes the cross-sectional evidence indicating that regional brain volumes, as measured by volumetric magnetic resonance imaging, are reduced in HD and are correlated with disease characteristics. We also evaluate the relationship between imaging measures and clinical change, their longitudinal change characteristics, and within-individual longitudinal associations of imaging with disease progression. This analysis will be valuable in assessing pharmacodynamics in clinical trials and supporting clinical outcome assessments to evaluate treatment effects on neurodegeneration.

A quantitative assessment of Parkinson’s disease (PD) progression is critical for optimizing clinical trials design. Disease progression model was developed using pooled data from the Progression Marker Initiative study and the Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation in Parkinson’s Disease study. Age, gender, concomitant medication, and study arms were predictors of baseline. A mutation in the leucine-rich repeat kinase 2 (LRRK2) encoding gene was associated with the disease progression rate. The progression rate in subjects with PD who carried LRRK2 mutation was slightly slower (~0.170 points/month) than that in PD subjects without the mutation (~0.222 points/month). For a non-enriched placebo-controlled clinical trial, approximately 70 subjects/arm would be required to detect a drug effect of 50% reduction in the progression rate with 80% probability. Whereas, 85, 93 and 100 subjects/arm would be required for an enriched clinical trial with 30%, 50% and 70% subjects with LRRK2 mutations, respectively.

The Critical Path for Parkinson's (CPP) Imaging Biomarker and Modeling and Simulation working groups aimed to achieve qualification opinion by the European Medicines Agency (EMA) Committee for Medical Products for Human Use (CHMP) for the use of baseline dopamine transporter neuroimaging for patient selection in early Parkinson's disease clinical trials. This paper describes the regulatory science strategy to achieve this goal. CPP is an international consortium of three Parkinson's charities and nine pharmaceutical partners, coordinated by the Critical Path Institute.

Time to RECIST progression (TTP) is currently used as a surrogate for patient benefit for either accelerated regulatory approval or to study the effects of factors such as drug exposure and drug target expression levels on RECIST progression-free survival (PFS). While the conventional paradigm assumes that increased antitumor effect results in a longer TTP and PFS, we show that this is often not the case due to a fundamental mathematical property of the RECIST definition of disease progression in which faster tumor shrinkage rate (k) can lead to paradoxically shorter TTP (1,2). We investigate under what conditions this paradoxical behavior occurs, what fraction of patients satisfy these conditions, and whether this phenomenon can invert population-level PFS hazard ratio. We use a mathematical model treating tumor burden as decreasing drug-sensitive and increasing drug-resistant cell subpopulations and fit this model to data from several clinical trials with different indications (3). We simulate a more effective treatment and record the resulting TTP. A study-level analysis compares the relationship of speed and depth of response with TTP20 for both the administered “control” and simulated “more effective” drug. Depending on indication, 3-27% of patients are estimated to have a paradoxically inverse relationship between k and TTP. We conclude that while RECIST progression criteria may be useful for decision making in patients, care must be taken in interpreting TTP as a metric of treatment efficacy due to the real possibility that a stronger treatment effect paradoxically results in shorter TTP. We propose and test an alternative benefit metric: the model-projected time that tumor size reaches 120% of baseline (TTB120). Simulated head-to-head studies show that in contrast to TTP, TTB120 is always consistent with patient benefit. We recommend that any alternative metrics with a time-to-event endpoint should consider the directional consistency between speed and depth of response that the endpoint is consistent with patient benefit under any set of patient conditions. References: 1. Eisenhauer EA et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J Cancer 2009;45(2):228-47. 2. Johnson K et al. Directional inconsistency between Response Evaluation Criteria in Solid Tumors (RECIST) time to progression and response speed and depth. Eur J Cancer 2019;109:196-203. 3. Project Data Sphere, https://www.projectdatasphere.org/projectdatasphere/html/home; 2018 [accessed 7 September 2018]. Citation Format: Kaitlyn E. Johnson, Axel Gomez, Jackson Burton, Douglas White, Arijit Chakravarty, Annette Schmid, Dean Bottino. An alternative time-to-event metric that is consistent with tumor response speed and depth, unlike RECIST time to progression [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 11.

Aim We seek to characterize how faster tumour shrinkage rate (k) can lead to paradoxically shorter Response Evaluation Criteria in Solid Tumors (RECIST) time to progression (‘TTP20’ – tumour size exceeding its minimum by 5 mm and 20%) [1] and, therefore, progression-free survival (PFS). Specifically, we investigate under what conditions this paradoxical behaviour occurs, what fraction of patients satisfy these conditions, whether this phenomenon can invert population-level PFS hazard ratio, and consistency of an alternative time-to-event benefit metric with k. Methods We use a mathematical model treating tumour burden as decreasing drug-sensitive and increasing drug-resistant cell subpopulations. We fit this model to data from several clinical trials with different indications [2]. We simulated a more effective treatment and recorded whether patients' TTP20 increased or decreased. We performed a study-level analysis to compare the relationship of speed and depth of response with TTP20 for both the administered ‘control’ and simulated ‘more effective’ drug. We propose and test an alternative benefit metric: the model-projected time that tumour size reaches 120% of baseline (TTB120). Results Depending on indication, 3–27% of patients are estimated to have a paradoxically inverse relationship between k and TTP20. Simulated head-to-head studies show that TTP20-based PFS can favour the less effective drug. In contrast, TTB120 always favours the more effective drug. Conclusion We demonstrate the paradoxical behaviour of RECIST TTP20 – as an exemplar of percent-change-from-nadir based cancer progression criterion – both in theory and in observed patient data at the individual and trial level. We propose an alternative tumour size–based criterion (TTB120) that is directionally consistent with tumour shrinkage rate.

e14022 Background: RECIST time to progression (RTTP) is the cornerstone of widely used surrogates for patient benefit such as RECIST progression-free survival (RPFS), which in turn is used either for accelerated regulatory approval or to study the effects of factors such as drug exposure and drug target expression levels on RPFS. While one might expect increased antitumor effect to result in longer RTTP and RPFS, we will show that this is often not the case due to a fundamental mathematical property of the RECIST definition of disease progression. Methods:We use a simple mathematical model that treats patient tumor burden dynamics as an evolutionary process consisting of a decreasing drug-sensitive subpopulation of tumor cells and an independently growing drug-resistant subpopulation. This model has successfully described tumor dynamics in NSCLC, melanoma (CPT Pharmacometrics Syst. Pharmacol. (2017) 6, 29–39), and ovarian cancer (internal data). In the presence of drug, we represent the net fitness of the sensitive cells in terms of an exponential decay or “kill” rate k > 0, and the net fitness of the resistant cells as an exponential growth rate g > 0, and the initial fraction of drug-resistant cells by 0 ≤ f ≤ 1. For a given virtual patient (parameter set), we simulate RTTP as the time after start of treatment at which tumor burden V(t) is 20% higher than its lowest value (per RECIST criteria). We then vary the kill rate k and check whether RTTP increases or decreases with increasing k. Results: There is a large set of parameter values for which RTTP is predicted to vary inversely with increasing kill rate k. These parameter values are attained clinically in ovarian cancer patients taking alisertib and paclitaxel (internal data). Conclusions: While RECIST progression criteria may be useful for patient care to identify when a patient is losing her response, care must be taken in interpreting RTTP as a metric of treatment effect due to the real possibility that a stronger treatment effect results in shorter RTTP. We recommend that alternative metrics for patient benefit, such as a generalization of Days Gained (Neal et al PLoS ONE 8(1)e51951), be evaluated. We will present results of these evaluations currently in progress.

Introduction What is the optimal “shape” for a drug pipeline: A flute? A megaphone? A trumpet? In other words, what set of stringencies for passage of candidates to the next stage in development maximizes the value of the pipeline as a whole, and how do assays predicting drug quality influence this optimum? Methods A linear programming formulation was developed for four phases of a drug pipeline: Preclinical, Phase I, Phase II, and Phase III, with variables including: the total budget for the entire pipeline (B), and the number (Ni), cost (ci), and net present value (vi) of drugs in each phase (i). The model is subject to two constraints: (1) the pipeline must reduce in size with each subsequent phase (i.e. Npre≥ N1 ≥ N2 ≥ N3), and (2) the sum total of the cost times the number of drugs in each phase must be less than or equal to the total budget (i.e. cpreNpre + c1N1 + c2N2 + c3N3 ≤ B). The overall goal is to maximize the total value of drugs progressing through the pipeline (i.e. maximize vtot = vpreNpre + v1N1 + v2N2 + v3N3). Additionally, pipeline evolution was modeled to evaluate the implications of a “predictive” drug quality assay. Hypothetical “drug quality scores” were drawn from normal distributions which reflect industry probabilities of success between phases. Drugs that are above a cutoff stringency advance forward, with the process repeated for all phases. The cost of each phase and value for successful drugs are recorded for each simulation. The process is repeated many times, with the expected value calculated at each iteration. Results Beginning with a pool of drug candidates, the optimal pipeline structure should approach equal sized pipeline widths as rapidly as possible. Monte Carlo simulations demonstrated that increasing the stringency of a “quality assay” in Phase I results in a linear increase in expected value. Additionally, if the quality test is improved, e.g. by reducing the variability of the assay, the improvement in expected value will increase in an exponential manner. Conclusions The simulations predict that to maximize pipeline value, drug companies should: (a) pick the winners as early as possible (i.e. Preclinical or Phase I), (b) set stringent go/no-go criteria in Preclinical and Phase I development, and (c) perform prospective clinical trial simulations (Preclinical to Phase I, Phase I to Phase II, Phase II to Phase III) to improve the ability to predict winners. Citation Format: Jackson Burton, William Antebi, Christopher J. Zopf, Dean Bottino, Shu-Wen Teng, Ryan Nolan, Arijit Chakravarty. A model-based approach toward clinical pipeline optimization. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1636. doi:10.1158/1538-7445.AM2015-1636

Introduction The “bystander effect” is the hypothesized process by which neighboring cancer cells are killed when free drug is released into the tumor environment by dying cells. The extent to which an antibody drug conjugate (ADC) elicits the bystander effect can have significant impact on the magnitude and dynamics of efficacy. Understanding the bystander effect though experimental methods (e.g. video-microscopy) are challenging; however, when supplemented with in silico modeling, mechanistic details can be elucidated. The purpose of this work is to develop a spatial-temporal in silico platform describing tumor cell progression in the presence of an ADC in order to quantify the bystander effect. Methods The model employs spatial-stochastic methods to simulate the time evolution of cell death resulting from ADCs and freely diffusing payload based on following parameters: drug to antibody ratio (DAR), linker stability, antigen concentration, ADC cell membrane permeability, ADC-antigen internalization, payload permeability, and payload potency. Specifically, a 2-D rectangular grid of tumor cells, ADCs, and payload are numerically implemented through binary pixel occupation of the grid. Governing rules are then assigned for: mobility for freely diffusing ADCs and payload, reaction rate for ADC binding to antigen-expressing cells, rate of cell death dependent on payload concentration, and internalization rate of payload into non-antigen expressing cells. The simulation is run until stopping criteria are met, e.g. all cells are killed. Baseline parameters can be optimized by fitting the model to in vitro experimental video-microscopy data. Results Experimental conditions were simulated with the model to characterize the influence of the aforementioned ADC and biological parameters on the bystander effect. Conclusions By incorporating the spatial aspect of ADC mechanics, spatially dependent metrics can be formulated to quantify the bystander effect. The model allows for wide adjustability of the ADC design parameters as well as relevant biological parameters. Thus, by understanding the relative parameter sensitivities, the model can explore design optimization of ADCs that maximize the bystander effect. Through such optimization, the magnitude and dynamics of drug exposure at the site of action can be controlled to maximize efficacy and improve the therapeutic window. Citation Format: Jackson Burton, Shu-Wen Teng, Christopher J. Zopf, Ryan Nolan, Arijit Chakravarty. An in silico platform for characterizing ADC bystander effects. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5337. doi:10.1158/1538-7445.AM2015-5337