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WO2025216806A1 - Optimisation d'essais de puissance - Google Patents

Optimisation d'essais de puissance

Info

Publication number
WO2025216806A1
WO2025216806A1 PCT/US2025/016585 US2025016585W WO2025216806A1 WO 2025216806 A1 WO2025216806 A1 WO 2025216806A1 US 2025016585 W US2025016585 W US 2025016585W WO 2025216806 A1 WO2025216806 A1 WO 2025216806A1
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WO
WIPO (PCT)
Prior art keywords
curve
dose
assay
line
predefined
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/US2025/016585
Other languages
English (en)
Inventor
Catherine CRUZ
Ahmad Shahir EBTIKAR
Delina KAMBO
Theodoro KOULIS
Jushen LIANG
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Genentech Inc
Original Assignee
Genentech Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Genentech Inc filed Critical Genentech Inc
Publication of WO2025216806A1 publication Critical patent/WO2025216806A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires

Definitions

  • a potency bioassay provides a measure of a response of a biological system to certain doses of a substance such as a drug. Such a measurement can establish that a given substance results in desired biological activity.
  • the potency of a given substance may be determined by way of performing sample potency assays for the substance.
  • a method comprises performing a first assay; generating a curve fitted to a plurality of first dose responses of the first assay; determining whether a deviation of individual ones of the first dose responses from the curve renders the first assay invalid; identifying a linear region of the curve; identifying a first endpoint and a second endpoint of the curve.
  • the method further comprises identifying a plurality of points on the curve between the first and second endpoints, the points identifying a corresponding plurality of second dose concentrations for a second assay.
  • a system comprises an assay that generates a plurality of dose responses based on a plurality of first dose concentrations, and at least one processor circuit with a memory.
  • the memory comprises instructions that, when executed by the processor circuit, cause the at least one processor circuit to at least generate a curve fitted to the first dose concentrations and dose responses of the assay; identify a linear region of the curve; identify a first endpoint and a second endpoint of the curve relative to the linear region of the curve; and identify a plurality of points on the curve between the first and second endpoints, the points identifying a corresponding plurality of second dose concentrations for a confirmatory assay.
  • a non-transitory, computer-readable medium comprises machine-readable instructions that, when executed by a processor of a computing device, cause the computing device to at least generate a parameter logistic curve fitted to a plurality of first dose concentrations and a corresponding plurality of dose responses generated from an assay; identify a linear region of the curve; identify a first endpoint and a second endpoint of the curve relative to the linear region of the curve; and identify a plurality of points on the curve between the first and second endpoints, the points identifying a corresponding plurality of second dose concentrations for a confirmatory assay.
  • FIG. 1 is a networked environment that includes a computing environment in accordance with the present disclosure.
  • FIG. 2 is a flowchart that illustrates a method for determining dose concentrations and dose responses for an potency assay in accordance with the present disclosure.
  • FIG. 3 is a graph that depicts the results from a potency assay in accordance with the present disclosure.
  • FIG. 4 is a flowchart that depicts functionality executed in the networked environment of FIG. 1 to generate a curve based of the potency assay of FIG. 3 in accordance with the present disclosure.
  • FIG. 5A is a is a graph of the potency assay depicted in FIG. 3 that includes a depiction of residual values relative to a curve in accordance with the present disclosure.
  • FIG. 5B is a graph that depicts a clustering of residual values as depicted in FIG. 5A of dose responses of past generated assays in accordance with the present disclosure.
  • FIG. 6 is a flowchart that depicts functionality executed in the networked environment of FIG. 1 to identify whether dose responses from the potency assay of FIG. 3 cluster with dose responses from past generated assays in accordance with the present disclosure.
  • FIG. 7 is a graph that depicts the potency assay as set forth in FIG. 3 that depicts an to attempt to adjust dose responses in accordance with the present disclosure.
  • FIG. 8 is a flowchart that depicts functionality executed in the networked environment of FIG. 1 to determine whether a deviation of the dose responses from the curve depicted in FIG. 7 renders an assay invalid in accordance with the present disclosure.
  • FIG. 9 is a graph that depicts an approach for identifying a linear portion of the curve of FIG. 3 in accordance with the present disclosure.
  • FIG. 10 is a graph that depicts an approach for identifying a set of optimal dose responses from the graph of FIG. 9 in accordance with the present disclosure.
  • FIG. 11 is a flowchart that depicts functionality executed in the networked environment of FIG. 1 to determine optimal dose concentrations and corresponding dose responses as depicted in FIGS. 9 and 10 in accordance with the present disclosure.
  • FIG. 12 is a flowchart that depicts one example of a method for determining dose concentrations and corresponding dose responses in accordance with the present disclosure.
  • an assay is typically performed on living cells or non-living subject matter other than living cells.
  • an assay may comprise a potency bioassay that is performed to determine the potency or effect of a drug or other substance with respect to its effect on biological subjects such as animals or plants.
  • an assay may comprise an Enzyme-linked immunosorbent assay or other type of assay.
  • the potency of a given drug or other substance may be indicated using a dose-response curve that represents the effect that such a drug or other substance has on a given biological process or another subject.
  • assays may be designed and performed. However, such a process can be time consuming and expensive involving the design and implementation of a large number of assays.
  • the networked environment 100 includes a computing environment 103, a client device 106, and potentially other devices that are in data communication with each other via a network 109.
  • the network 109 may comprise, for example, the Internet, intranets, extranets, wide area networks (WANs), local area networks (LANs), wired networks, wireless networks, or other suitable networks, etc., or any combination of two or more such networks.
  • WANs wide area networks
  • LANs local area networks
  • wired networks wireless networks, or other suitable networks, etc., or any combination of two or more such networks.
  • the client device 106 is representative of any one of a plurality of client devices that may be coupled to the network 109.
  • the client device 106 may comprise, for example, a processor-based system such as a computer system.
  • a computer system may be embodied in the form of a desktop computer, a laptop computer, personal digital assistants, cellular telephones, smartphones, tablet computer systems, or other devices with like capability.
  • the client device 106 may include a display that may comprise, for example, one or more devices such as liquid crystal display (LCD) displays, gas plasma-based flat panel displays, organic light emitting diode (OLED) displays, electrophoretic ink (E ink) displays, LCD projectors, or other types of display devices, etc.
  • LCD liquid crystal display
  • OLED organic light emitting diode
  • E ink electrophoretic ink
  • the computing environment 103 may comprise, for example, one or more computing devices 113 such as a server computer or other computing device.
  • the computing environment 103 may employ a plurality of computing devices 113 such as server computers that may be arranged, for example, in one or more server banks, computer banks, or other arrangements.
  • Such computing devices 113 may be located in a single installation or may be distributed among many different geographical locations.
  • the computing environment 103 may include a plurality of computing devices 113 that together may comprise a hosted computing resource, a grid computing resource and/or any other distributed computing arrangement.
  • the computing environment 103 may comprise an elastic computing resource where the allotted capacity of processing, network, storage, or other computing-related resources may vary over time.
  • the multiple computing devices 113 can provide for parallel processing to the extent that a greater number of computing operations are needed.
  • Each computing device 113 includes at least one processor circuit, for example, having a processor 116 and a memory 119, both of which are coupled to a local interface 123.
  • the local interface 123 may comprise, for example, a data bus with an accompanying address/control bus or other bus structure as can be appreciated.
  • the memory 119 is defined herein as including both volatile and nonvolatile memory and data storage components. Volatile components are those that do not retain data values upon loss of power. Nonvolatile components are those that retain data upon a loss of power.
  • the memory 119 may comprise, for example, random access memory (RAM), read-only memory (ROM), hard disk drives, solid-state drives, USB flash drives, memory cards accessed via a memory card reader, floppy disks accessed via an associated floppy disk drive, optical discs accessed via an optical disc drive, magnetic tapes accessed via an appropriate tape drive.
  • RAM random access memory
  • ROM read-only memory
  • hard disk drives solid-state drives
  • USB flash drives USB flash drives
  • memory cards accessed via a memory card reader floppy disks accessed via an associated floppy disk drive
  • optical discs accessed via an optical disc drive magnetic tapes accessed via an appropriate tape drive.
  • the memory 119 comprises various forms of non-transitory, computer-readable media as can be appreciated.
  • the memory 119 Stored in the memory 119 are various applications that are executable by a processor 116 in the computing environment 103. Also, various data may be stored in a memory 119 in the computing environment 103 and accessed by one or more applications as will be described.
  • the applications stored in the memory 119 and executable by the processor 116 comprise, for example, a curve fitting application 133, a dose response clustering application 135, a dose adjustment application 136, a dose optimization application 139, and potentially other applications.
  • an operating system and other applications may be stored in the memory 119 and executable by the processor 116 as can be appreciated.
  • the curve fitting application 133 generates a curve representing potency of a drug product or other substance with respect to certain biological activity that is under test.
  • Potency of a substance with respect to certain biological activity may be expressed in the form of a 4 Parameter Logistic (4PL) curve, 5 Parameter Logistic (5PL) curve, or other curve.
  • biological activity may comprise, for example, a mechanism of action where exposure of a substance such as a drug to a biological sample results in the generation of a given biological output.
  • Such an output may set forth a dose response of the biological mechanism of action to a given dose concentration.
  • the curve fitting application 133 is configured to input assay data 143 that comprises the dose concentrations and corresponding dose responses from the performance of an assay and generate a curve such as a 4PL curve or other appropriate curve using one of a number of curve fitting approaches to approximately fit the results of the assay as will be described.
  • the dose response clustering application 135 identifies respective ones of the dose responses that may need to be adjusted due to the fact that they vary relative to the curve generated by the curve fitting application 133 by an unacceptable residual value as will be described.
  • the dose optimization application 139 generates optimized assay data 146 from the curve by implementing an optimization approach as will be described.
  • the optimized assay data 146 may comprise, for example, a plurality of dose concentrations with their corresponding dose responses and other data.
  • the assay data 143 may include dose responses that fall outside of acceptable parameters relative to the 4PL curve, 5PL curve, or other curve required for a given potency assay in accordance with various manufacturing and governmental standards and practices.
  • the dose adjustment application 136 attempts to adjust various ones of the dose responses and the corresponding dose concentrations from the assay data 143 relative to the 4PL curve, 5 PL curve, or other curve in an attempt to generate a potency assay that falls within acceptable parameters as will be described.
  • the applications executed in the computing environment 103 may interact with local applications on the client device 106. For example, data input into the client device 106 by way of a local application executed on the client device 106 may be sent to a respective application executed on the computing environment 103. Alternatively, the applications executed in the computing environment 103 may be executed in a standalone computing device (not shown). Various devices may be employed to input data into the client device 106 or other computing device such as keyboards, touch screens, or other input devices. Also, any outputs or indications provided to users as described herein may be generated on a display device or other output device.
  • FIG. 2 shown is a flowchart that depicts a method 160 for generating a potency assay for a given drug or other substance as applied to a given biological subject or other subject.
  • first dose concentrations are determined for an assay in order to be able to generate a curve such as a 4PL curve.
  • the first dose concentrations may be determined based on experience and other factors.
  • the assay is performed to obtain dose responses for the first dose concentrations for a given biological subject.
  • a curve such as a 4PL, 5PL, or other curve
  • the first dose concentrations and the corresponding dose responses have variation such that it is not possible to fit a proper curve thereto.
  • dose concentrations may result in dose responses that do not increase or decrease but approximate a horizontal line which would not allow the creation of a proper 4PL curve, 5PL curve, or other appropriate curve. If a proper curve cannot be fitted to the first dose concentrations and the corresponding dose responses, the method 160 moves to box 173 in which an array of new dose concentrations are determined for a new assay.
  • the method 160 reverts back to box 166 as shown to perform a subsequent assay.
  • multiple assays may be redesigned and performed until a proper curve such as a 4PL, 5PL, or other appropriate curve can be fitted to the results of a given assay.
  • the curve fitting application 133 (FIG. 1 ) is implemented to fit a curve to the results of the assay.
  • the curve may be a 4PL curve, 5PL curve, or other appropriate curve.
  • any dose responses that do not cluster with the historical dose response data are marked as needing adjustment.
  • problematic dose responses are ones that do not cluster with historical clustering data as will be described. This may be the case, for example, due to the natural variability of biological systems.
  • box 183 if certain ones of the dose responses of the assay performed in box 166 have been marked as not clustering with the historical dose response data, then the method 160 moves to box 186. Otherwise, the method 160 proceeds to box 189.
  • the dose adjustment application 136 is implemented in an attempt to adjust the marked ones of the dose responses and their corresponding dose concentrations so that they cluster with historical dose response data. Thereafter, in box 193 if the attempted adjustment of any problematic dose response fails such that any one of the marked ones of the dose responses cannot be successfully adjusted to cluster with the historical dose response data, then the method 160 reverts back to box 173 to determine new dose concentrations for a new assay to start the process anew. [0044] However, in box 193, assuming that the adjustment of all marked ones of the dose responses from the assay obtained in box 166 was successful, the method 160 moves to box 189.
  • the dose optimization application 139 is implemented based on the curve fitted to the results of an assay to determine a set of optimal dose concentrations.
  • the optimized dose concentrations and corresponding optimized dose responses determined using the dose optimization application 139 should constitute a proper potency assay. If there are problematic dose responses from the assay that cannot be corrected as will be described, then the assay may need to be redesigned to start the process anew.
  • FIG. 3 shown is a graph 203 that depicts an example of results of a potency assay obtained from performing the assay in box 166 (FIG. 2).
  • a number of dose responses 206 result from corresponding dose concentrations applied to a biological subject.
  • the dose concentrations may be specified in an attempt to provide a number of dose responses 206 that fall in a linear region as is depicted in a curve 209 such as a 4PL curve, 5PL curve, or other similar curve.
  • FIG. 4 shown is a flowchart that provides one example of the operation of the curve fitting application 133. It is understood that the flowchart of FIG.
  • FIG. 4 provides merely an example of the many different types of functional arrangements that may be employed to implement the operation of the curve fitting application 133 as described herein. As an alternative, the flowchart of FIG. 4 may be viewed as depicting an example of elements of a method implemented in the computing environment 103 (FIG. 1 ).
  • the curve fitting application 133 inputs the dose concentrations and the corresponding dose responses 206 (FIG. 3) generated from the potency assay performed in box 166 (FIG. 2).
  • the dose concentrations and corresponding dose responses 206 may be entered through an application on the client device 106 (FIG. 1 ) and provided as an input to the curve fitting application 133 executed in the computing environment 103 via the network 109 (FIG. 1 ).
  • the dose concentrations and corresponding dose responses 206 from a single assay may be input to the curve fitting application 133.
  • some subjects of various assays may experience more variation in the magnitude of the dose responses 206.
  • the respective normalized dose responses 206 from multiple assays may be averaged for each dose concentration to obtain a value that more representative of the subject matter of the assay and less subject to variation that occurs from one assay to the next.
  • the resulting dose concentrations and their corresponding averaged dose responses 206 may be used as input to the curve fitting application 133 and treated in the manner set forth below for an individual assay.
  • a curve 209 (FIG. 3) is fit to the dose concentrations and dose responses 206.
  • the curve 209 may be, for example, a parameter logistic curve such a 4PL curve, 5PL curve, or other appropriate type of curve. This may be accomplished by using an appropriate curve fitting application adapted to fit parameter logistic curves to given points like the dose responses 206.
  • the curve 209 is normalized to appear on a scale of 0 to 1 . For example, for a 4PL fit, the upper asymptote is assigned to one and the lower asymptote is assigned to zero.
  • a desired curve cannot be generated that fits the dose concentrations and corresponding dose responses
  • the curve fitting application 133 proceeds to box 226 in which an indication is output that a desired curve 209 such as a 4PL curve, 5PL curve, or other appropriate curve could not be created or fit to the respective dose concentrations and dose responses 206.
  • the attempt to fit a curve 209 to the dose concentrations and corresponding dose responses 206 may not succeed. Such might be the case, for example, where the dose responses 206 are flat across the set of increasing or decreasing dose concentrations.
  • the curve fitting application 133 proceeds to box 229 to output the data representing the curve 209 such as a 4PL curve, 5PL curve, or other curve to facilitate further processing by other applications as will be described.
  • data representing the curve 209 such data may be stored in a memory 119 (FIG. 1 ) to be accessed for further processing. Thereafter, the curve fitting application 133 ends as shown.
  • FIG. 5A shown is a further example of a graph 203 that depicts the dose responses 206 (FIG. 3) from the assay and the curve 209 (FIG. 3).
  • the individual dose responses 206 vary or deviate from the curve 209 by respective residual values R.
  • a determination is made as to whether a given residual value R between the position on the Y axis of a given dose response 206 and a corresponding position of the curve 209 on the Y axis falls within a plot of a cluster of residual values from normalized historical prior assays.
  • FIG. 5B shown is a graph 231 that depicts a plot of residual values 233 relative to a zero axis.
  • the graph 231 plots the residual values 233 from many prior performed assays.
  • the dose responses 206 FIG. 3
  • the curve fitting application 133 FIG. 1
  • a residual value between a given dose response 206 and the curve generated by the curve fitting application 133 falls within a cluster of acceptable residual values obtained from prior performed assays that are performed on biological subjects that have characteristics that are similar to the biological subject of the assay that generates the dose responses 206.
  • various clustering algorithms may be employed to determine whether a given residual value R clusters with the acceptable residual values depicted in the graph 231 .
  • the data that makes up the graph 231 is obtained from data of many different assays performed previously using biological subjects or other subjects and drugs or other substances such that the cluster of residual values 233 is relevant to the current biological or nonbiological subject.
  • a clustering analysis is performed to determine whether the residual value associated with a given dose response 206 clusters with the previous acceptable residual values of dose responses 206 from prior assays.
  • clustering analysis might be performed using, for example, a hierarchical densitybased clustering algorithm.
  • FIG. 6 shown is a flowchart that provides one example of the operation of the dose response clustering application 135. It is understood that the flowchart of FIG. 6 provides merely an example of the many different types of functional arrangements that may be employed to implement the operation of the dose response clustering application 135 as described herein. As an alternative, the flowchart of FIG. 6 may be viewed as depicting an example of elements of a method implemented in the computing environment 103 (FIG. 1 ).
  • the dose response clustering application 135 is executed to identify dose responses 206 (FIG. 3) that deviate from an expected dose response on the curve 209 (FIG. 3) by a residual value R (FIG. 5A) that is greater than an acceptable residual value.
  • the residual value R is determined to be within acceptable limits when the residual value R clusters with acceptable residual values as depicted in FIG. 5B described above.
  • the dose response clustering application 135 identifies a first one of the dose responses 206 for processing to determine whether the dose response 206 is acceptable.
  • the dose response clustering application 135 determines if a residual value R associated with current identified dose response 206 clusters with the historical residual value R data from prior assays. In box 237, if the dose response 206 does not cluster, then the dose response clustering application 135 proceeds to box 238. Otherwise, the dose response clustering application 135 moves to box 239. In box 238, the current designated dose response 206 is marked for adjustment. The dose response clustering application 135 then proceeds to box 239 as shown.
  • box 239 it is determined if the last dose response 206 has been considered. If not, then the dose response clustering application 135 proceeds to box 241 to identify the next dose response 206 for consideration. Thereafter, the dose response clustering application 135 reverts back to box 236 as shown. Otherwise, the dose response clustering application 135 ends as shown.
  • FIG. 7 shown is an example of a graph 253 that depicts the dose responses 206 from the assay and the curve 209 as was depicted in FIG. 3 above.
  • a spline fit curve 256 comprising a spline fit to the dose responses 206 is further depicted in the graph 253.
  • the spline fit curve 256 is generated using a monotone piecewise cubic interpolation approach, although other interpolation techniques that employ monotonicity and that are continuous may be used.
  • the individual dose responses 206 may vary or deviate from an expected dose response on the curve 209 by a residual value R.
  • the dose adjustment application 136 (FIG. 1 ) is executed in an attempt to adjust the position of respective ones of the dose responses 206 that deviate from an expected dose response along the curve 209 by various residual values R, where such deviation indicates that the assay is not suitable for providing dose concentrations for a given drug or other substance.
  • the dose adjustment application 136 attempts to adjust the dose responses 206 that deviate unacceptably by generating multiple different alternative dose responses 259 in multiple iterations, where each alternative dose response 259 may deviate from the curve 209 by an alternative residual value RA.
  • TO identify whether a deviation by a given residual value R or alternative residual value R renders the assay unsuitable a determination is made as to whether the residual value R or alternative residual value RA between the position of a given dose response 206 or alternative dose response 259 and a corresponding position on the curve 209 falls within a plot of a cluster of residual values from normalized historical prior assays as will be described.
  • a given dose response 206 is shifted in increments along the spline fit curve 256 to generate alternative dose responses 259 in either direction.
  • a determination is made as to whether a resulting alternative residual value RA at a respective alternative dose response 259 falls within the cluster of residual values from the normalized historical prior assays.
  • the spline fit curve 256 may be divided into a predefined number of sections.
  • the total number of sections may be, for example, 100,000 or some other number of sections.
  • the maximum distance that a dose response 206 is shifted to identify an alternative dose response 259 is, for example, 20 sections in either direction from the given dose response 206.
  • the total number of sections may be specified as a number other than 100,000 and the maximum distance that a dose response 206 may be shifted is a number other than 20.
  • the assay may be deemed unworkable and the assay may be redesigned before performing a subsequent assay and beginning the process anew.
  • the assay may be deemed unworkable and the assay may be redesigned before performing a subsequent assay and beginning the process anew.
  • FIG. 8 show is a flowchart that provides further functionality of the dose adjustment application 136 that attempts to adjust one or more dose responses 206 (FIG. 7) having residual values R that are unacceptable such that the respective assay is unsuitable for use.
  • the dose adjustment application 136 identifies a first one of the doses that has been marked for adjustment in box 238 (FIG. 6). Thereafter, in box 266 the dose adjustment application 136 specifies an alternative dose response 259 (FIG. 7) by moving a position of the current dose response 206 (FIG. 7) under consideration along the spline fit curve 256 (FIG. 7) by a predefined distance to the right or left.
  • the spline fit curve 256 may be divided into a predefined number of sections as set forth above
  • the current dose response 206 under consideration may be shifted a predefined number of sections of the spline fit curve 256 to the right or to the left.
  • the dose response 206 may be moved among sections within a predefined distance of the current dose response 206 randomly or in some other manner.
  • an alternative residual value RA associated with the current alternative dose response 259 falls within an acceptable residual value cluster generated from historical acceptable residual value data from prior assays.
  • the other dose responses 259 that move along the spline fit curve 256 together along with the respective dose response 259 being adjusted may be evaluated to see if their respective alternative residual values R fall within an acceptable residual value cluster generated from historical acceptable residual value data from prior assays.
  • the dose adjustment application 136 proceeds to box 279 in which it is determined whether a maximum number of iterations to consider alternative dose responses 259 have been performed.
  • the maximum number of iterations is set to a value of, for example, 20 iterations on either side of a given dose response 206. The reason for setting a maximum number of iterations is because if a respective one of the dose responses 206 having a residual value RA that does not fall within the acceptable residual value cluster is moved too far, then the chances are greater that the corresponding assay is not repeatable or has other issues.
  • the dose adjustment application 136 proceeds to box 283 in which an indication that at least one of the dose responses 206 cannot be corrected is rendered on a display device or other output device, thereby indicating that the assay is not repeatable or has other issues.
  • the potency assay may need to be redesigned in a further attempt to obtain an assay that is repeatable and suitable to characterize the dose response of a given drug or other substance. In such case, the entire process may be started once again with a new potency assay. Thereafter, the functionality of the dose adjustment application 136 ends as shown.
  • the dose adjustment application 136 proceeds to box 286 in which it is determined whether there are any further dose responses 206 that were marked for adjustment. If so, then the dose adjustment application 136 moves to box 289. Otherwise, the dose adjustment application 136 proceeds to box 293.
  • box 289 the next dose response 206 that has been marked for adjustment is identified. Thereafter, the dose adjustment application 136 reverts back to box 266 to repeat the process with respect to the next marked dose response 206.
  • the dose adjustment application 136 has proceeded to box 293, then an indication that the various problematic dose responses 206 have been successfully adjusted is rendered on an output device such as, for example, a display device to inform users that the dose concentrations and the corresponding dose responses 206 are acceptable for use in characterizing a given drug or other substance. Thereafter, the functionality of the dose adjustment application 136 ends as shown.
  • an output device such as, for example, a display device to inform users that the dose concentrations and the corresponding dose responses 206 are acceptable for use in characterizing a given drug or other substance.
  • one or more dose responses 206 from a given assay may deviate from the curve 209 by a residual value R. Such a deviation may indicate a given assay as invalid requiring a redesign of the assay.
  • To determine whether the deviation would indicate that a given assay is invalid it is determined whether a residual value R of individual ones of the dose responses 206 or alternative residual values RA of individual ones of the alternative dose responses 259 relative to the curve 209 clusters with the residual values 233 (FIG. 5B) associated with historical dose response data.
  • the historical dose response data may be considered predefined data obtained from various prior assays. In this manner, it is possible to determine whether a given assay will be acceptable or needs to be redesigned before optimized dose responses 206 are determined.
  • determining whether such a deviation of one or more of the dose responses 206 from the curve 209 renders the assay invalid it may be determined whether a residual value R of the individual ones of the dose responses 206 relative to the curve 209 clusters with predefined dose response data as was described in box 236.
  • determining whether the deviation of individual ones of the dose responses 206 from the curve 209 renders the assay invalid may further comprise attempting to correct one or more of the dose responses 206 that deviates from the curve 209 beyond an acceptable residual value R.
  • the spline fit curve 256 is fit to the dose responses 206. Then, one or more iterations of moving a position of the one of the dose responses 206 along the spline fit curve 256 by a predefined distance to specify an alternative dose response 259. A determination is made as to whether an alternative residual value RA associated with the alternative dose response 259 relative to a corresponding expected dose response on the curve 209 for the a given iteration falls within an acceptable residual value cluster. In one example, the one or more iterations may comprise a maximum of 20 iterations.
  • a plurality of alternative residual values R may be identified that are associated with a corresponding plurality of alternative dose responses 259.
  • individual ones of the alternative residual values RA are associated with corresponding ones of the iterations.
  • an assay is deemed invalid if none of the alternative residual values RA falls within the acceptable residual value cluster.
  • the dose responses 206 of a given assay may be determined to be valid without having to run a large number of assays.
  • the assay may be deemed unworkable and the assay may need to be redesigned before performing a subsequent assay and beginning the process anew. In this manner, it is possible to attain a potency assay that is repeatable and meets required regulatory standards while reducing the number of test assays performed.
  • FIG. 9 shown is a graph 303 of an example of a curve 209 that is generated by the curve fitting application 133 (FIG. 4).
  • the dose optimization application 139 (FIG. 1 ) is executed to determine optimized dose concentrations and corresponding dose responses 206 (FIG. 3) that ideally fall on the curve 209.
  • a linear region of the curve 209 is identified first by specifying a predefined point 306 that represents half of the maximal response on a transition portion 309 of the curve 209.
  • the transition portion 309 of the curve 209 is the part of the curve 209 that begins from a lower asymptote 313 and extends to an upper asymptote 316.
  • the predefined point 306 may comprise a midpoint of the transition portion 309 of the curve 209.
  • a first line 319 is specified as tangent to the curve 209 at the predefined point 306.
  • the first line 319 intersects the curve 209 at the predefined point 306 and at both a first intersection 323 and a second intersection 326 as shown.
  • a second line 329 is specified as extending from a point close to the origin (0, 0) on the graph 303 through the intersection 323 between the first line 319 and the curve 209 below the predefined point 306.
  • the y-value at the point close to the origin (0, 0) is calculated at an effective concentration (EC) of 0.01 or ECO.01 .
  • the second line 329 extends from a point on the lower asymptote 313 at an effective concentration (EC) that approaches zero or has a value of less than 0.05, or some other value as will be described.
  • a first region 333 is specified on the graph 303 that is bounded by the first line 319 and the curve 209.
  • a second region 336 is specified on the graph 303 that is bounded by the second line 329 and the curve 209.
  • the first line 319 is rotated clockwise about the predefined point 306 by changing or reducing the slope by predefined slope decrements, thereby varying the area of the first region 333 and the second region 336.
  • the linear region X of the curve 209 is defined as the portion of the curve 209 that falls between the intersections of the first line 319 and the curve 209 above and below the predefined point 306 at a specific slope of the first line 319 where a sum of the area of the first region 333 and the second region 336 is a minimum.
  • the linear region X of the curve 209 may be defined by a first boundary 339 and a second boundary 343, where the first boundary 339 is a vertical line extending through the first intersection 323 and the second boundary 343 is a vertical line extending through the second intersection 326.
  • the second line 329 extends from a point on the lower asymptote 313 at an effective concentration (EC) that approaches zero or some other value as will be described.
  • the second line 329 extends from the point on the lower asymptote 313 having an effective concentration below a predefined threshold where the minimum in the sum of the area of the first region 333 and the second region 336 can be calculated. That is to say, the effective concentration of the point on the lower asymptote 313 where the second line 329 begins cannot be too high such that the desired minimum in the sum of the area of the first region 333 and the second region 336 cannot be obtained.
  • the effective concentration of the point near the origin (0, 0) may be specified as less than a predefined threshold, where the predefined threshold is defined as the maximum effective concentration at which a minimum in the sum can be determined as described herein.
  • the predefined threshold is defined as the maximum effective concentration at which a minimum in the sum can be determined as described herein.
  • the first intersection 323 moves along the curve 209 toward the lower asymptote 313 and the second intersection 326 moves along the curve 209 toward the upper asymptote 316.
  • the first and second regions 333 and 336 change and become larger or smaller depending on the specific location of the first intersection 323 at a particular slope increment.
  • FIG. 10 shown is a graph 346 that depicts an additional view of the curve 209.
  • the linear region X of the curve 209 is determined as described above. Once the linear region X of the curve 209 is known, then right and left endpoints 349a and 349b are determined. This is done by determining left and right expansion regions Y on both the left side and the right side of the linear region X.
  • Each of the expansion regions Y are specified as a proportional expansion of the linear region X. In one example, the ratio of the width of the linear region X to the width of each one of the expansion regions Y is 3/7 to 2/7. Alternatively, the ratio of the width of the linear region X to the width of the expansion regions Y may be some other ratio.
  • the right and left endpoints 349a and 349b are located on the curve 209 at the outer edges of the expansion regions Y. Once the right and left endpoints 349a and 349b are known, a number of points 353 on the curve 209 between the first and second endpoints 349a and 349b are determined. The points 349a/b along the curve 209 set forth desired dose responses, with the corresponding dose concentrations along the X axis. The dose concentrations may be employed to implement a second potency assay to confirm the dose responses.
  • the number of points 353 identified between the first and second endpoints 349a and 349b may vary. In one example, there may be 8 points 353 between the endpoints 349a and 349b. Alternatively, some other number of points 353 may be specified depending on the number of dose concentrations desired for the second potency assay.
  • the curve 209 may be separated into equal segments between the respective points 349a/b.
  • an appropriate arc length equation may be employed to determine the length of each segment between the respective pairs of the points 349a/b.
  • FIG. 11 shown is a flowchart that provides one example of the operation of the dose optimization application 139. It is understood that the flowchart of FIG. 11 provides merely an example of the many different types of functional arrangements that may be employed to implement the operation of the dose optimization application 139 as described herein. As an alternative, the flowchart of FIG. 11 may be viewed as depicting an example of elements of a method implemented in the computing environment 103 (FIG. 1 ).
  • the dose optimization application 139 is executed in order to obtain optimal dose concentrations that result in a consistent and robust assay dose- response curve for a given drug or other substance. Such a dose-response curve may be required for regulatory purposes and other purposes. The dose response curve is impacted by various controls within a given potency assay as can be appreciated.
  • the dose optimization application 139 To generate optimal dose concentrations, the dose optimization application 139 first identifies a linear region X (FIG. 10) of the curve 209 (FIG. 10). Thereafter, the endpoints 349a and 349b (FIG. 10) are identified on the curve 209 by identifying expansion regions Y on the curve that are a proportional expansion of the linear region X (FIG. 10) as described above. Once the endpoints 349a and 349b are known, then the dose optimization application 139 identifies a predetermined number of points 353 (FIG. 10) on the curve 209 representing desired dose responses 206 with their corresponding dose concentrations for an assay.
  • a predetermined number of points 353 FIG. 10
  • a predefined point 306 (FIG. 9) is identified on the curve 209 that is generated by the curve fitting application 133 (FIG. 1 ) described above.
  • the curve 209 may comprise a 4PL curve, 5PL curve, or other curve that is suitable to represent a dose response for a given drug or substance.
  • the predefined point 306 may be specified as a center or an approximate center of a transition portion 309 (FIG. 9) of the curve 209 as described above. As such, the predefined point 306 may also be termed a center point of the curve 209.
  • a first line 319 (FIG. 9) is specified on the graph 303 (FIG. 9) that is tangent to the curve 209 at the predefined point 306 on the transition portion 309 of the curve 209.
  • the first line 319 intersects the curve 209 at a first intersection 323 (FIG. 9) near the lower asymptote 313 (FIG. 9) of the curve 209 and at a second intersection 326 (FIG. 9) near the upper asymptote 316 (FIG. 9) of the curve 209.
  • a second line 329 (FIG. 9) is specified on the graph 303 that extends from a point close to the origin (0, 0) of the graph as described above and passing through the first intersection 323.
  • a first region 333 (FIG. 9) is specified on the graph 303 of the curve 209 that is bounded by the first line 319 and the curve 209 below the predefined point 306 on the curve 209. Stated another way, the first region 333 is the area between the predefined point 306 and the first intersection 323 below the predefined point 306 on the curve 209.
  • a second region 336 (FIG. 9) is specified on the graph 303 of the curve 209 that is bounded by the second line 329 and the curve 209 between the origin (0, 0) of the graph 303 and the first intersection 323.
  • the second region 336 is positioned between a point near the origin (0, 0) of the graph 303 and the first intersection 323.
  • the dose optimization application 139 calculates a sum of the area of the first region 333 and the second region 336.
  • the sum that is calculated is stored in a memory in the computing environment 103.
  • the first line 319 is rotated by changing or reducing a slope of the first line 319 in predefined slope decrements as described above, the sum of the first and second regions 333 and 336 will initially begin to fall until a minimum area is reached. Thereafter, the sum of the first and second regions 333 and 336 will begin to rise.
  • a current sum may be compared with a prior sum. If the current sum is less than the prior sum, then a minimum will not have been reached. However, if the current sum is greater than the prior sum, then the prior sum would represent a minimum in the sum of the area of the first and second regions 333 and 336.
  • the dose optimization application 139 proceeds to box 426 in which the first line 319 is rotated clockwise by changing a slope of the first line 319 by a predetermined slope decrement.
  • the magnitude of the predetermined slope decrement is specified, for example, to have enough resolution to be able to effectively detect the minimum in the sum of the first and second regions 333 and 336 with predefined precision.
  • the magnitude of the predetermined slope decrement may be specified to have over 100, 200, or more iterations between the beginning slope tangent to the curve 209 at the predefined point 306 to a zero slope. Thereafter, the dose optimization application 139 reverts back to box 419 to calculate the next sum of the first and second regions 333 and 336.
  • the dose optimization application 139 proceeds to box 429 in which a first boundary 339 (FIG. 9) and a second boundary 343 (FIG. 9) of the linear region X are defined as corresponding to or passing through the first intersection 323 and the second intersection 326, respectively, between the first line 319 and the curve 209 at the slope increment where the area of the sum of the first and second regions 333 and 336 is minimized.
  • a first boundary 339 (FIG. 9) and a second boundary 343 (FIG. 9) of the linear region X are defined as corresponding to or passing through the first intersection 323 and the second intersection 326, respectively, between the first line 319 and the curve 209 at the slope increment where the area of the sum of the first and second regions 333 and 336 is minimized.
  • the dose optimization application 139 moves to box 433 to identify the location of endpoints 349a and 349b on the curve 209, where the endpoints 349a and 349b define an area of interest of the curve 209 therebetween.
  • This is done by adding the expansion regions Y (FIG. 10) onto both sides of the linear region X, where the width of each respective one of the expansion regions Y is proportional to a width of the linear region X.
  • the ratio of the width of the linear region X to a respective one of the expansion regions is generally 3/7:2/7 although some other ratio may be employed as described above. That is to say, the ratios may vary by a small amount and still remain effective at capturing the endpoints.
  • a plurality of points 353 (FIG. 10) on the curve 209 are identified between the first and second endpoints 349a and 349b.
  • the number of points 353 may first be determined.
  • the number of points may be specified based on standards used for potency assays.
  • the total number of points 353 may be 8 or some other number of points.
  • the number of points 353 may depend upon the number of wells used in a given assay. Alternatively, the number of points 353 may be four or greater.
  • the curve 209 between the endpoints 349a and 349b may be separated into equal segments using, for example, an arc length equation as mentioned above.
  • the number of equal segments depends on the total number of points 353 including the first and second endpoints 349a and 349b specified on the curve 209.
  • the placement of the respective points 353 between each adjacent pair of the equal length segments between the endpoints 349a/349b may be determined.
  • a second assay also termed a confirmatory assay herein. That is to say, a second assay may be run using the dose concentrations associated with the endpoints 349a/349b and the points 353 to confirm that such dose concentrations produce the respective dose responses such that the assay is valid where the resulting dose-response curve is robust and repeatable to adequately represent the potency of a given drug or other substance. Such dose-response curves may be employed, for example, for regulatory purposes and other purposes. [0109] Thereafter, in box 439 the optimal dose concentrations and the corresponding dose responses are stored in the memory(ies) 119 as the optimized assay data 146 (FIG. 1). Then, the dose optimization application 139 ends as shown.
  • FIG. 12 shown is a flowchart of one example method 500 according to the present disclosure.
  • the method 500 depicted in FIG. 12, as well as other examples set forth herein, provide one or more advantages or benefits including obtaining potency assays that have a high degree of confidence that they are performing the way they should and that characterize a drug or other substance while reducing the number of potency assays performed to identify the proper dose concentrations for such assays. Also, those potency assay designs that will not result in potency assays that can adequately characterize a given drug or other substance may be identified much easier, thereby minimizing the number of assays that are performed.
  • assays such as bioassays measure the biological activity of a drug therapeutic and are critical to ensuring the quality of drugs that are given to patients. Therefore, it is desired that bioassay performance be robust, accurate, and precise.
  • the selection of dose concentrations may be an important factor with respect to bioassay performance as dose response curves are used to measure the biological activity of a drug relative to a reference.
  • the present disclosure describes employing an empirical approach to designing the optimal dose concentrations and incorporates data analysis comparisons to prior historical bioassay data that has been successfully employed for assay performance. Together, this tool will select the optimal dose response curve using less overall number of manual assays run and empirically determine the dose concentrations thereby removing human bias when selecting points.
  • the method 500 begins with performing a first assay. Then, in step 506, a curve is generated that is fitted to a plurality of first dose responses of the first assay. In step 508 it is determined whether a deviation of individual ones of the dose responses from the curve renders the assay invalid. Next, in step into 509 a linear region of the curve is identified. Then, step 513 involves identifying a first endpoint and a second endpoint of the curve. Thereafter, in step 516, a plurality of points are identified on the curve between the first and second endpoints, the points identifying a corresponding plurality of second dose concentrations for a second assay. Step 519 involves performing a second assay using the plurality of second dose concentrations associated with corresponding ones of the points on the curve to produce a corresponding plurality of second dose responses.
  • Clause 1 is a method, comprising performing a first assay, generating a curve fitted to a plurality of first dose responses of the first assay, and determining whether a deviation of individual ones of the first dose responses from the curve renders the first assay invalid.
  • the method further comprises identifying a linear region of the curve, identifying a first endpoint and a second endpoint of the curve, and identifying a plurality of points on the curve between the first and second endpoints, the points identifying a corresponding plurality of second dose concentrations for a second assay.
  • Clause 2 is a method as set forth in claim 1 , further comprising performing the second assay using the plurality of second dose concentrations associated with corresponding ones of the points on the curve to produce a corresponding plurality of second dose responses.
  • Clause 2.1 is a method as set forth in clause 1 , wherein the curve comprises a 4PL curve.
  • Clause 3 is a method as set forth in any one of clauses 1 through 2.1 , wherein the determining whether the deviation of the individual ones of the dose responses from the curve renders the assay invalid further comprises determining whether a residual value of the individual ones of the first dose responses relative to the curve clusters with predefined dose response data.
  • Clause 4 is a method as set forth in any one of clauses 1 through 3, wherein the identification of the linear region of the curve further comprises specifying a first line that is tangent to the curve at a predefined point on a transition portion of the curve; specifying a first region that is bounded by the first line and the curve; specifying a second region that is bounded by a second line and the curve; rotating the first line about the predefined point on the curve by changing a slope of the first line by a plurality of predefined slope decrements, thereby varying an area of the first and second regions; identifying one of the predefined slope decrements where a sum of the area of the first and second regions is minimized; and defining a first boundary and a second boundary of the linear region as corresponding to a first intersection and a second intersection, respectively, between the first line and the curve at the one of the predefined slope decrements.
  • Clause 5 is a method as set forth in clause 4, further comprising identifying the predefined point as an approximate center of the transition portion of the curve.
  • Clause 6 is a method as set forth in clauses 4 or 5, further comprising specifying the second line extending from a point near an origin and passing through the first intersection.
  • Clause 7 is a method as set forth in clauses 4 through 6, wherein the predefined slope decrements are calculated as 200 even decrements between the slope of the first line as it is tangent to the curve at the predefined point to a slope of zero.
  • Clause 8 is a method as set forth in clauses 4 through 7, wherein the identification of the plurality of points on the curve between the first and second endpoints further comprises separating the curve into a plurality of equal length segments using an arc length equation.
  • Clause 9 is a method as set forth in clauses 1 through 8, wherein the determining whether the deviation of individual ones of the dose responses from the curve renders the assay invalid further comprises attempting to correct one of the dose responses that deviates from the curve beyond an acceptable residual value.
  • Clause 10 is a method as set forth in clause 9, wherein the curve further comprises a first curve, and wherein the attempt to correct the one of the second dose responses that deviates from the curve by a residual value beyond the acceptable residual value further comprises fitting a second curve to the dose responses; performing at least one iteration of moving a position of the one of the dose responses along the second curve by a predefined distance to specify an alternative dose response; and determining if an alternative residual value associated with the alternative dose response relative to a corresponding expected dose response on the curve for the at least one iteration falls within an acceptable residual value cluster.
  • Clause 11 is a method as set forth in clause 10, wherein the at least one iteration further comprises a plurality of iterations.
  • Clause 12 is a method as set forth in clause 11 , wherein a number of the iterations comprises a maximum of 20.
  • Clause 13 is a method as set forth in clause 12, wherein the alternative residual value further comprises a plurality of alternative residual values and the alterative dose response further comprises a plurality of alternative dose responses, and individual ones of the alternative residual values is associated with a corresponding one of the iterations, the method further comprising determining that the first assay is invalid if none of the alternative residual values falls within the acceptable residual value cluster.
  • Clause 14 is a system, comprising an assay that generates a plurality of dose responses based on a plurality of first dose concentrations, and at least one processor circuit with a memory comprising instructions that, when executed by the processor circuit, cause the at least one processor circuit to at least generate a curve fitted to the first dose concentrations and dose responses of the assay, and identify a linear region of the curve.
  • the instructions further cause the at least one processor circuit to at least identify a first endpoint and a second endpoint of the curve relative to the linear region of the curve, and identify a plurality of points on the curve between the first and second endpoints, the points identifying a corresponding plurality of second dose concentrations for a confirmatory assay.
  • Clause 15 is a system as set forth in clause 14, wherein the curve comprises a 4PL curve.
  • Clause 16 is a system as set forth in any one of clauses 14 or 15, wherein the instructions, when executed by the processor circuit, further cause the at least one processor circuit to at least attempt to correct at least one of the dose responses, the at least one of the dose responses deviating from an expected dose response on the curve greater than an acceptable residual value.
  • Clause 17 is a system as set forth in any one of clauses 14 through 16, wherein the instructions, when executed by the processor circuit, that cause the at least one processor circuit to identify the linear region of the curve further comprise instructions causing the at least one processor circuit to at least specify a first line that is tangent to the curve at a predefined point on a transition portion of the curve; specify a first region that is bounded by the first line and the curve; specify a second region that is bounded by a second line and the curve; rotate the first line about the predefined point on the curve by changing a slope of the first line by a plurality of predefined slope decrements, thereby varying an area of the first and second regions; identify one of the predefined slope decrements where a sum of the area of the first and second regions is minimized; and define a first boundary and a second boundary of the linear region as corresponding to a first intersection and a second intersection, respectively, between the first line and the curve at the one of the predefined slope decrements.
  • Clause 18 is a system as set forth in clause 17, wherein the predefined point is identified as an approximate center of the transition portion of the curve.
  • Clause 19 is a system as set forth in any one of clauses 17 or 18, wherein the second line extends from near an origin point and passes through the first intersection.
  • Clause 20 is a system as set forth in any one of clauses 17 through 19, wherein a number of the predefined slope decrements is specified to have a resolution to be able to detect a minimum in the sum of the area of the first and second regions with a predefined precision.
  • Clause 21 is a system as set forth in any one of clauses 14 through 20, wherein the identification of the plurality of points on the curve between the first and second endpoints further comprises separating the curve into a plurality of equal length segments between the first and second endpoints.
  • Clause 22 is a non-transitory, computer-readable medium, comprising machine-readable instructions that, when executed by a processor of a computing device, cause the computing device to at least generate a parameter logistic curve fitted to a plurality of first dose concentrations and a corresponding plurality of dose responses generated from an assay; identify a linear region of the curve; identify a first endpoint and a second endpoint of the curve relative to the linear region of the curve; and identify a plurality of points on the curve between the first and second endpoints, the points identifying a corresponding plurality of second dose concentrations for a confirmatory assay.
  • Clause 23 is a non-transitory, computer-readable medium as set forth in clause 22, wherein the parameter logistic curve comprises a 4PL curve.
  • Clause 24 is a non-transitory, computer-readable medium as set forth in any one of clauses 22 or 23, wherein the machine-readable instructions, when executed by the processor of the computing device, further cause the computing device to determine whether a deviation of individual ones of the dose responses from corresponding ones of a plurality of expected dose responses on the parameter logistic curve renders the assay invalid.
  • Clause 25 is a non-transitory, computer-readable medium as set forth in any one of clauses 22 through 24, wherein the machine-readable instructions, when executed by the processor of the computing device, cause the computing device to identify the linear region of the parameter logistic curve, further cause the computing device to at least specify a first line that is tangent to the parameter logistic curve at a predefined point on a transition portion of the parameter logistic curve; specify a first region that is bounded by the first line and the parameter logistic curve; specify a second region that is bounded by a second line and the parameter logistic curve; rotate the first line about the predefined point on the parameter logistic curve by changing a slope of the first line by a plurality of predefined slope decrements, thereby varying an area of the first and second regions; identify one of the predefined slope decrements where a sum of the area of the first and second regions is minimized; and define a first boundary and a second boundary of the linear region as corresponding to a first intersection and a second intersection, respectively, between the
  • Clause 26 is a non-transitory, computer-readable medium as set forth in clause 25, wherein the predefined point is identified as an approximate center of the transition portion of the parameter logistic curve.
  • Clause 27 is a non-transitory, computer-readable medium as set forth in any one of clauses 25 or 26, wherein the second line extends from near an origin point and passes through the first intersection.
  • Clause 28 is a non-transitory, computer-readable medium as set forth in any one of clauses 22 through 27, wherein the identification of the plurality of points on the parameter logistic curve between the first and second endpoints further comprises separating the parameter logistic curve into a plurality of equal length segments between the first and second endpoints.
  • Disjunctive language such as the phrase “at least one of X, Y, or Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., can be either X, Y, or Z, or any combination thereof (e.g., X; Y; Z; X or Y; X or Z; Y or Z; X, Y, or Z; etc.).
  • X Y
  • Z X or Y
  • Y or Z X or Z

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Abstract

L'invention divulgue divers exemples pour la détermination optimale de points de réponse de dose pour un essai. Un premier essai est effectué et une courbe qui est générée est ajustée à une pluralité de réponses de dose du premier essai. On détermine si un écart de réponses individuelles parmi les premières réponses de dose à partir de la courbe rend le premier essai invalide. Une région linéaire et des premier et second critères de jugement de la courbe sont identifiés. Une pluralité de points est identifiée sur la courbe entre les premier et second critères de jugement. Les points identifient une pluralité correspondante de secondes concentrations de dose pour un second essai.
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Publication number Priority date Publication date Assignee Title
US20230352151A1 (en) * 2017-12-24 2023-11-02 Ventana Medical Systems, Inc. Computational pathology approach for retrospective analysis of tissue-based companion diagnostic driven clinical trial studies

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230352151A1 (en) * 2017-12-24 2023-11-02 Ventana Medical Systems, Inc. Computational pathology approach for retrospective analysis of tissue-based companion diagnostic driven clinical trial studies

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