cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.4445777.v37
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision making process by explicitly identifying the necessary, albeit elaborate, information and assumptions.
DOI: 10.6084/m9.figshare.4445777.v38
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4445777.v39
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision making process by explicitly identifying the necessary, albeit elaborate, information and assumptions.
DOI: 10.6084/m9.figshare.4445777.v40
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4445777.v41
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision making process by explicitly identifying the necessary, albeit elaborate, information and assumptions.
DOI: 10.6084/m9.figshare.4445777.v42
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4445777.v43
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision making process by explicitly identifying the necessary, albeit elaborate, information and assumptions.
DOI: 10.6084/m9.figshare.4445777.v44
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4445777.v45
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision making process by explicitly identifying the necessary, albeit elaborate, information and assumptions.
DOI: 10.6084/m9.figshare.4445777.v46
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4445777.v47
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision making process by explicitly identifying the necessary, albeit elaborate, information and assumptions.
DOI: 10.6084/m9.figshare.4445777.v48
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4445777.v49
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision making process by explicitly identifying the necessary, albeit elaborate, information and assumptions.
DOI: 10.6084/m9.figshare.4445777.v51
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision making process by explicitly identifying the necessary, albeit elaborate, information and assumptions.
DOI: 10.6084/m9.figshare.4445777.v50
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12907643
title: R0195_20171005a_videos

description: Rat 0195, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.4443254
title: Microwave assisted solid phase catalyst-free Biginelli synthesis of 3,4-dihydropyrimidin-2(1<i>H</i>)-one and 3,4-dihydropyrimidin-2(1<i>H</i>)-thione: A green approach, characterization and molecular crystal structures

description: Solid phase catalyst-free Biginelli synthesis of novel 5-carbethoxy-4-(3-bromo-4-hydroxy-5-methoxyphenyl)-6-methyl-3,4-dihydropyrimidin-2(1H)-one (C<sub>15</sub>H<sub>17</sub>BrN<sub>2</sub>O<sub>5</sub>) and 5-carbethoxy-4-(3-bromo-4-hydroxy-5-methoxyphenyl)-6-methyl-3,4-dihydropyrimidin-2(1H)-thione (C<sub>15</sub>H<sub>17</sub>BrN<sub>2</sub>O<sub>4</sub>S) has been carried out under microwave irradiation at 400W. The structures of both compounds were confirmed by <sup>1</sup>H NMR, <sup>13</sup>C NMR, Mass, FT-IR, Elemental analysis, and single crystal X-ray diffraction method. The single crystals of both compounds were obtained by crystallization in 0.35 × 0.30 × 0.25 mm dimension for (C<sub>15</sub>H<sub>17</sub>BrN<sub>2</sub>O<sub>5</sub>) and 0.35 × 0.32 × 0.30 mm dimension for (C<sub>15</sub>H<sub>17</sub>BrN<sub>2</sub>O<sub>4</sub>S). Both compounds, (C<sub>15</sub>H<sub>17</sub>BrN<sub>2</sub>O<sub>5</sub>) and (C<sub>15</sub>H<sub>17</sub>BrN<sub>2</sub>O<sub>4</sub>S) crystallizes in the monoclinic P2<sub>1</sub>/c space group and shows four and two intermolecular hydrogen bonds, respectively.