Method of measuring efficacy of treatment for multiple sclerosis

Abstract

A method of measuring efficacy of treatment of multiple sclerosis (MS) in an animal in a vivarium is described. Animal activity data is collected at multiple times during the night. Sequential time regions of the night are identified as high-activity, activity-drop, or low-activity regions. Embodiments are described to quantify a drop, during the night, of an animal's activity level. These quantified activity-drop scalars for consecutive nights are accumulated in an animal health dataset. This dataset is compared to healthy animals or a standard of care to determine efficacy. One embodiment quantifies an activity-drop by fitting straight-line curves through the data in the three nightly regions. Another embodiment uses a Fourier transform on a circle and a linear combination. Another embodiment compares areas under data curves in the regions. Animals may be housed in cages with other animals.

We claim: 
     
         1 . A method of measuring efficacy of a first treatment of multiple sclerosis (MS) in an animal in a vivarium comprising the steps of:
 (a) placing a study animal in a cage in the vivarium and enrolling the study animal in a study;   (b) electronically observing one or more animal activities in real-time of the study animal using a combination of electronic cameras, infrared (IR) lighting of the study animals, and electronic hardware including computation and communication hardware;   (c) selecting a single “LASSO metric” with an associated scalar “activity-drop value”;   (d) selecting an “MS health detection function,” wherein an input to the MS health detection function comprises an animal health dataset; and wherein an output of the MS health detection function comprises an animal health severity scalar for the animal;   (e) collecting a set of nightly activity data for the animal comprising a plurality of activity scalars for the animal each night, wherein the nightly activity scalars are collected repeatedly and continually throughout a night;   (f) computing a LASSO best-fit of a first piece-wise linear function to the nightly activity data, generating a LASSO L0, L1 and L2;   (g) applying the LASSO metric to the generated LASSO L0, L1 and L2, generating a nightly activity-drop value;   (h) adding the nightly activity-drop value into the “animal health dataset,” wherein the animal health dataset comprises the nightly activity-drop values;   (i) applying the MS health detection function to the animal health dataset;   (j) iterating steps (e) through (i) for sequential nights until a terminating condition is reached;   wherein a first measured efficacy of the first treatment comprises comparing the animal health severity scalars from step (i) to animal health severity scalars from a reference treatment;   (k) removing the study animal from the study when the terminating condition is reached.   
     
     
         2 . The method of  claim 1  wherein:
 the reference treatment comprises treating with a naïve vehicle. 
 
     
     
         3 . The method of  claim 1  wherein:
 the reference treatment comprises a standard of care. 
 
     
     
         4 . The method of  claim 1  wherein:
 the collecting step (e) starts at induction of the animal into a study; and 
 wherein the terminating condition in step (k) comprises the animal reaching an onset of MS. 
 
     
     
         5 . The method of  claim 4  further comprising:
 (l) repeating steps (e) through (j) with multiple animals in a first cohort; 
 computing a second measured efficacy of the treatment comprising an average of the animal health severity scalars from each step (i) for each animal in the cohort. 
 wherein at least some of the multiple animals in the first cohort are housed in home cages in the vivarium wherein the home cages contain more than one animal; 
 wherein the (e) collection step of the animals is performed on animals in their home cage. 
 
     
     
         6 . The method of  claim 1  wherein:
 the animal health severity scalar comprises a predicted worst case of health of the animal in a time window after an onset of MS in the animal. 
 
     
     
         7 . The method of  claim 1  further comprising the step:
 (m) measuring a baseline activity level of the animal prior to induction; 
 wherein the MS health detection function comprises a further input comprising the baseline activity level of the animal prior to induction. 
 
     
     
         8 . The method of  claim 7  further wherein:
 the MS health detection function comprises a percentage responsive to normalization using the baseline activity level of the animal prior to induction. 
 
     
     
         9 . The method of  claim 1  further comprising the steps:
 (n) detecting an onset of MS of the animal; 
 (o) treating the animal with the first treatment following onset. 
 
     
     
         10 . The method of  claim 1  further comprising:
 (p) repeating steps (e) through (j) with multiple animals in a first cohort; 
 (q) computing a second measured efficacy of the first treatment comprising an average of the severity values from each step (i) for each animal in the cohort. 
 
     
     
         11 . The method  claim 1  comprising the additional steps:
 (r) locating an intermediate point within the activity-drop region in the night; 
 (s) computing a first best-fit straight-line curve through a region surrounding the intermediate point; 
 (t) computing a second best-fit straight-line curve for the high-activity region; 
 (u) computing a third best-fit straight-line curve for the low-activity region; 
 (v) computing a first point midway between (the intersection point of the first and second best-fit straight-lines curves, a “second point”) and (the intersection point of the first and third best-fit straight-lines curves, a “third point”); 
 wherein steps (r) through (v) are the activity-drop metric; 
 wherein the high-activity region is updated to the region from the start of the night to the second point; the low-activity region is updated to begins at the third point; and the activity-drop region is updated to the region between the second and third points; and 
 wherein the activity value of the first point is the activity-drop value for the night. 
 
     
     
         12 . The method  claim 11  wherein comprising the additional steps:
 (w) iterating steps (r) through (v) until a terminating condition is reached; 
 wherein the new intermediate point in step (r) for each iteration is at the activity-drop value of the prior iteration; 
 wherein the region surrounding the intermediate point is the activity-drop region from the prior iteration; 
 wherein the first, second and third best-fit straight-line curves are recomputed each iteration; 
 wherein the first, second and third points are recomputed each iteration; and 
 wherein the activity value of the first point of the last iteration is the activity-drop value for the night. 
 
     
     
         13 . The method  claim 1  wherein comprising the additional steps:
 (x) averaging the nightly activity data for a first initially predetermined period beginning at the beginning of the night, the “starting region;” 
 (y) averaging the nightly activity data for a second initially predetermined period ending at the end of the night, the “ending region;” 
 (z) computing a first best-fit straight-line curve from end of the starting region to the start of the ending region; 
 (aa) computing a second best-fit straight-line curve through the starting region; 
 (bb) computing a third best-fit straight-line curve through the ending region; 
 (cc) computing a first point midway between (the intersection point of the first and second best-fit straight-lines curves, a “second point”) and (the intersection point of the first and third best-fit straight-lines curves, a “third point”); 
 wherein steps (x) through (cc) are the activity-drop metric; 
 wherein the high-activity region is updated to the region from the start of the night to the second point; the low-activity region is updated to start at the third point and the activity-drop region is updated to the region between the second and third points; and 
 wherein the activity value of the first point is the activity-drop value for the night. 
 
     
     
         14 . The method  claim 13  wherein comprising the additional steps:
 (dd) iterating steps (x) through (cc) until a terminating condition is reached; 
 wherein the new starting region for each iteration is the high-activity period of the prior iteration; 
 wherein the new ending region for each iteration is the low-activity period of the prior iteration; 
 wherein the first, second and third best-fit straight-line curves are recomputed each iteration; and 
 wherein the first, second and third points are recomputed each iteration; 
 wherein the activity value of the first point of the last iteration is the activity-drop value for the night. 
 
     
     
         15 . The method  claim 12  wherein:
 the MS health detection function is responsive to the activity-drop value for at least one night. 
 
     
     
         16 . The method  claim 15  wherein:
 wherein the MS health detection function is computed, at least in part, using the additional method steps: 
 (ee) executing at least a subset of method steps (a) through (w) repeatedly using different animals and different treatments and observing the actual efficacy of the each treatment; 
 (ff) modifying the MS health detection using a best-fit method such that the MS health detection function best determines the observed actual efficacy of the each different treatment. 
 
     
     
         17 . A method of measuring efficacy of a first treatment of multiple sclerosis (MS) in an animal in a vivarium comprising the steps of:
 (gg) placing a study animal in a cage in the vivarium and enrolling the study animal in a study;   (hh) electronically observing one or more animal activities in real-time of the study animal using a combination of electronic cameras, infrared (IR) lighting of the study animals, and electronic hardware including computation and communication hardware;   (ii) selecting a single “linear combination coefficient set” that generates a scalar linear combination value when applied responsively to a set of discreet transform values;   (jj) selecting an “MS health detection function” whose input comprises an animal health dataset and whose output comprises an animal health severity scalar for the animal;   (kk) collecting an activity scalar of the animal repeatedly and continually for a night: a set of “nightly activity data”;   (ll) computing a Fourier transform on a circle responsive to the nightly activity data, generating a set of discreet transform values;   (mm) applying the linear combination coefficient set responsively to the set of discreet transform values; wherein the resulting scalar linear combination value is a nightly activity value;   (nn) adding the nightly activity value into the animal health dataset; wherein the animal health dataset comprises the nightly activity values;   (oo) applying the MS health detection function to the animal health dataset, generating an animal health severity scalar for the animal;   (pp) iterating steps (kk) through (oo) for sequential nights until a terminating condition is reached;   wherein a first measured efficacy of the first treatment comprises comparing the animal health severity scalars from step (kk) to those from a reference treatment.   
     
     
         18 . The method of  claim 17 , wherein:
 the health detection function is responsive to a difference between a nightly activity value in the animal health dataset and a baseline activity value.   
     
     
         19 . The method of  claim 18 , wherein:
 the collecting in step (kk) occurs during an animal's recovery period after the animal has MS; and   wherein the baseline activity value is responsive to an activity value during an animal's baseline time period prior to induction into a study.   
     
     
         20 . The method of  claim 18 , wherein:
 the collecting in step (kk) occurs during the animal's recovery period after the animal has MS; and   wherein the baseline activity value is responsive to a minimum activity level value after induction into a study.   
     
     
         21 . A method of measuring efficacy of a first treatment of multiple sclerosis (MS) in an animal in a vivarium comprising the steps of:
 (qq) placing a study animal in a cage in the vivarium and enrolling the study animal in a study;   (rr) electronically observing one or more animal activities in real-time of the study animal using a combination of electronic cameras, infrared (IR) lighting of the study animals, and electronic hardware including computation and communication hardware;   (ss) selecting an “MS health detection function” whose input comprises an animal health severity scalar;   (tt) collecting an activity scalar of the animal repeatedly and continually for a night: generating a set of “nightly activity data”;   (uu) computing a baseline low-activity level responsive to the set of nightly activity data;   (vv) modifying the set of nightly activity data by subtracting the baseline low-activity level from the elements of the set;   (ww) identifying automatically two sequential regions in the nightly activity data for each night: a “high-activity region,” and a “low-activity region”;   (xx) computing a high-activity value for each night equal to the integration of values in the set of nightly activity data taken within the time interval of the high-activity region;   (yy) adding the high-activity value for each night into the “animal health dataset,” wherein the animal health dataset comprises the resulting high-activity values;   (zz) applying the MS health detection function to the animal health dataset;   (aaa) iterating steps (tt) through (zz) for sequential nights until a terminating condition is reached;   wherein a first measured efficacy of the first treatment comprises comparing the animal health severity scalars from step (bbb) to those from a reference treatment.   
     
     
         22 . The method of  claim 21  further comprising steps:
 (bbb) computing a low-activity value for each night equal to the integration of values in the set of nightly activity data within the time interval of the low-activity region; 
 (ccc) adding the low-activity value into the “animal health dataset,” wherein the animal health dataset comprises the resulting nightly activity-drop values; 
 wherein step (aaa) is performed before or after step (ww); 
 wherein step (bbb) is performed before or after step (xx); and 
 wherein early MS health detection function is responsive to both the high-activity values and the low-activity values in the animal health dataset. 
 
     
     
         23 . The method of  claim 21  wherein:
 the MS health detection function is further responsive to an activity level of the animal prior to an induction date for the animal into a study. 
 
     
     
         24 . method of  claim 23  wherein:
 the MS health detection function is normalized responsive to the activity level of the animal prior to an induction date for the animal into the study. 
 
     
     
         25 . The method of  claim 21  wherein:
 the early MS health detection function is responsive only to activity for only a single night.