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WO2000022437A1 - Methode de pronostic pour traitement anti-resorption - Google Patents

Methode de pronostic pour traitement anti-resorption Download PDF

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WO2000022437A1
WO2000022437A1 PCT/US1999/020698 US9920698W WO0022437A1 WO 2000022437 A1 WO2000022437 A1 WO 2000022437A1 US 9920698 W US9920698 W US 9920698W WO 0022437 A1 WO0022437 A1 WO 0022437A1
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bone
level
bap
bmd
therapy
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WO2000022437A9 (fr
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Claude Darte
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Hybritech Inc
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/573—Immunoassay; Biospecific binding assay; Materials therefor for enzymes or isoenzymes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • the invention relates generally to anti-resorptive treatments of postmenopausal women, and specifically to methods of assessing the long-term efficacy of anti-resorptive treatments in individual postmenopausal women. Description of the Prior Art
  • Osteoporosis is a disease characterized by a low bone mass and architectural deterioration of bone tissue. Osteoporosis leads to increased susceptibility to fracture. Decreased bone mass is one of the main determinants of fracture. Two reasons may cause decreased bone mass: first, an imbalance between bone resorption and bone formation within a remodeling unit due to increased osteoclastic activity and/or decreased osteoblastic activity; second, an increase in the activation frequency, i.e., in the number of remodeling units initiated per unit of time and space. Increased bone turnover resulting from postmenopausal estrogen deficiency is the main determinant of bone loss and can be non-invasively assessed by measuring through serum and/or urine biochemical markers of bone turnover (1,2).
  • Anti-resorptive therapy such as estrogen replacement therapy (HRT) and bisphosphonate treatment
  • HRT estrogen replacement therapy
  • bisphosphonate treatment have been shown to decrease bone turnover, preventing postmenopausal bone loss and significantly reducing fracture risk both in early and late postmenopausal women (3-6).
  • HRT estrogen replacement therapy
  • bisphosphonate treatment has been shown to decrease bone turnover, preventing postmenopausal bone loss and significantly reducing fracture risk both in early and late postmenopausal women (3-6).
  • HRT estrogen replacement therapy
  • bisphosphonate treatment have been shown to decrease bone turnover, preventing postmenopausal bone loss and significantly reducing fracture risk both in early and late postmenopausal women (3-6).
  • HRT estrogen replacement therapy
  • bisphosphonate treatment bisphosphonate treatment
  • One aspect of the present invention provides a method for predicting bone mass response and compliance of an individual after an anti- resorptive therapy.
  • the method basically includes the following steps:
  • a bone marker i.e., serum bone alkaline phosphatase (BAP)
  • BAP serum bone alkaline phosphatase
  • a logistic algorithm based on both the level of bone marker after a first predetermined time period and the change of bone marker level from the baseline is utilized to predict a probability of response in bone mass after a longer predetermined period of therapy, i.e., two (2) years.
  • a logistic algorithm based on both the level of bone marker at the baseline and the change of bone marker level from the baseline after a predetermined period is utilized to predict a probability of response in bone mass after a longer predetermined period of therapy, i.e., two (2) years.
  • a cutoff can be selected by a receiver-operating characteristic (ROC) curve analysis to provide corresponding sensitivity and specificity.
  • the cutoff is a function of the change of the bone marker level at a predetermined time period and the level of bone marker at either the baseline or the predetermined time period.
  • the present invention method utilizes a logistic regression model.
  • the BMD data may be plotted on a two-dimensional diagram.
  • One dimension of the two-dimensional diagram is bone marker level, and the other dimension the change of bone marker level from the baseline.
  • Methods of the present invention provide a number of advantages. As explained in greater detail below, it has been found that methods of the present invention can quickly and accurately identify non-responders from responders to anti-resorptive treatment. For example, it can provide about 72% of sensitivity to predict two-year lumbar spine BMD response or to distinguish placebo from alendronate-treated patient for a given 90% specificity. Therefore, the methods of the present invention provide higher diagnostic specificity and sensitivity than the methods which consider the BAP level and BAP change parameters alone.
  • non-responders or patients who do not adequately take alendronate
  • identification of such patients after a few months of treatment would be useful to insure that alendronate is taken appropriately.
  • identification of a positive response to alendronate might improve long-term compliance.
  • the methods of the present invention are well suited for use during the anti-resorptive treatment for assessing the treatment response and the compliance of the treatment. They may also be applied to other markers of bone formation and bone resorption with different characteristics in terms of reproducibility and responses to alendronate. Furthermore, the methods of the present invention may also be applied to other anti-resorptive therapies such as estrogen replacement therapy.
  • FIGURE 1 is a diagram which shows the response of bone alkaline phosphatase to treatment with alendronate (10 mg/day) or placebo in 307 elderly osteoporotic women.
  • FIGURE 2 is a diagram which shows the relationship between the percentage change from the baseline after six months of treatment of BAP, BAP level after six months of treatment) and the predicted probability of lumbar BMD positive response (after twenty-four months of treatment) as computed by logistic regression in 307 elderly osteoporotic women.
  • Responders are identified as patients with a percent increase in BMD from baseline after twenty-four months of treatment > 3%.
  • No BMD change was defined as a percent BMD change between -3% and +3%.
  • BMD non-responders were women with a bone loss greater than 3%.
  • the predicted probabilities were computed for differentiating BMD responders from both no BMD change and BMD non-responders considered as a single group.
  • FIGURES 3A and 3B are diagrams which show the areas under the ROC curve for the prediction of BMD (Fig. 3A) and alendronate-treated patients (vs. placebo, Fig. 3 B) in 307 elderly osteoporotic women.
  • Three predictive models were compared: BAP percent change from baseline, BAP level, and their combination by logistic regression.
  • ROC curves were established for the three discriminants and the areas under the curves computed as a function of the treatment monitoring time. P-values refer to the significance level of the difference between those areas as indicated.
  • FIGURE 4 is a diagram which shows the prediction of lumbar spine BMD response to twenty-four months of treatment, based on either BAP percent change (from baseline after six months of treatment) or BAP level (after six months of treatment) or their combination by logistic regression in 307 elderly osteoporotic women. Discriminant cutoff values were selected to provide 90% specificity for non-response prediction for each model, so that resulting sensitivities for positive response prediction could be compared. BMD responders, no BMD change and BMD non-responders were defined as in
  • FIGURE 5 is a diagram which shows the relationship between the percentage change from the baseline after six months of treatment of BAP, BAP level at the baseline and the predicted probability of lumbar BMD positive response (after twenty-four months of treatment) as computed by logistic regression in 307 elderly osteoporotic women.
  • FIGURE 6 is a diagram which shows the prediction of lumbar spine BMD response to twenty-four months of treatment, based on either BAP percent change (from baseline after six months of treatment) or BAP level (at the baseline) or their combination by logistic regession in 307 elderly osteoporotic women. Discriminant cutoff values were selected to provide 90% specificity for non-response prediction for each model, so that resulting sensitivities for positive response prediction could be compared. BMD responders, no BMD change and BMD non-responders were defined as in Figure 2. Sensitivity and specificity figures apply for the discrimination of BMD responders from BMD non- responders and no BMD change considered as a single group.
  • the present invention is based on the discovery of a new model, based on the logistic combination of the level of bone marker and its percentage change in a patient at six months, to predict soon after initiating alendronate therapy those patients who will not significantly improve their BMD after two years of treatment.
  • the present invention is also based on the discovery of a defined cutoff value that can easily be used in clinical practice to identify individual non-responder patients after only six months of treatment.
  • one aspect of the present invention provides a method for predicting a treatment response and compliance of an individual after an anti- resorptive therapy.
  • the method comprises the steps of: a. measuring a baseline of a bone marker in the individual at the beginning of the anti-resorptive therapy; b. measuring a level of the bone marker in the individual after a first predetermined time period of anti-resorptive therapy; and c. generating a probability of response in bone mass after a second predetermined time period of the anti-resorptive therapy from a change of the bone marker level from the baseline and the level of bone marker at either the baseline or at the first predetermined time period for the purpose of determining the treatment response and compliance.
  • an anti-resorptive therapy is a therapy that decreases bone turnover, prevents bone loss and reduces fracture risk.
  • an anti-resorptive therapy include, but are not limited to, bisphosphonate therapy or estrogen replacement therapy (HRT).
  • the anti-resorptive therapy is alendronate treatment therapy.
  • a bone marker may be any biochemical compound that can be used as a mark to reflect any changes in bone formation and bone resorption. Therefore, a bone marker can be any marker of bone formation and bone resorption.
  • a bone alkaline phosphatase (BAP) is used as a bone marker.
  • BAP is from serum; although, other bone markers from other sources derived from human body parts may also be used, e.g., urine samples.
  • a level of a bone marker in a patient may be measured by using conventional methods that are known to those skilled in the art.
  • a two-site immunoradiometric assay using two monoclonal antibodies directed against the human bone isoenzyme may be used.
  • Such a two-site immunoradiometric assay is known to one skilled in the art, and procedures can be obtained from the manufacturer Hybritech Incorporated (San Diego, California).
  • Other known methods for measuring bone markers include high performance liquid chromatography (HPLC), lectin precipitation, heat inactivation and immunoadsorption.
  • the first predetermined time period is a time period within which the decrease of a bone marker has reached a plateau. Such time may vary depending on how fast a bone marker responds to a treatment. For an anti-resorptive therapy, this period may be three to six months. Preferably, for alendronate treatment, a BAP level at the six months treatment is measured. It should be understood that earlier time points may also be valuable, and one skilled in the art can readily determine the time period for measuring a bone marker without undue experimentation in view of the present disclosure.
  • a response in bone mass may be a change in bone mineral density
  • BMD bone mass
  • a change in BMD is used as a response in bone mass for the anti-resorptive therapy.
  • bone mass from lumbar spine is measured although bone mass from other organs such as, but not limited to, femoral neck and forearm may also be measured.
  • a second predetermined time period of the anti-resorptive therapy is a time that a response in bone mass is sufficiently significant so that the responsiveness of the individual undergoing the anti-resorptive therapy can be determined.
  • a BMD change at two years of anti-resorptive therapy may be used to indicate whether a patient is a responder to the treatment. It will be understood that different time points may also be valuable.
  • a responder is defined as an individual, for example a woman, demonstrating a BMD increase after two years of treatment of 3% or more.
  • a BMD change between -3% and +3% is considered as no significant change and an individual with a bone loss greater than 3% is considered as a non-responder.
  • a probability of a response in bone mass after a second predetermined time period of the anti-resorptive therapy may be generated by a logistic algorithm, preferably, a logistic regression algorithm, based on a change of the bone marker level from the baseline at a first predetermined period and the level of bone marker at either the baseline or the first predetermined time period.
  • a logistic algorithm may be used to compute the statistical significance levels of each parameter estimated in the logistic equation.
  • a logistic algorithm model may be evaluated based on the maximum likelihood estimation and Chi-square tests.
  • a logistic regression model is appropriate only when the predicted probability ((p)-level) associated with a Chi-square and the slopes of each variable in the logistic equation are statistically significant (for example, p ⁇ 0.05).
  • a cutoff may be selected to provide a corresponding sensitivity and specificity of the prediction of the present invention.
  • a cutoff may be established by a receiver-operating characteristic (ROC) curve analysis.
  • the cutoff (t) is a function of two variables, i.e., BAP percentage change from baseline and BAP level at either the baseline or at six months, according to the following logistic equation:
  • Equation [3] indicates that for any particular t value, the cutoff corresponds to a straight line when patient's bone marker data is reported in two-dimension scatter-plots where axes represent the two variables, i.e., BAP percentage change from baseline (X) and absolute BAP level at either baseline or at six months (Y).
  • This straight line separates positive from negative data points with a sensitivity and specificity that are set when the cutoff value t is selected by ROC curve analysis.
  • the individual patient's BMD data can be scatter-plotted on a two-dimensional diagram, where one dimension is serum BAP level at six months, and the other is percentage change of serum BAP level at six months.
  • the individual patient's BMD data may be scatter-plotted on a two-dimensional diagram where one dimension is serum BAP level at the baseline and the other is percentage change of serum BAP level at six months.
  • the "responsive" or BMD "response” is defined as an increase of the BMD by three percent (3%) or more over the long-term, i.e., two (2) years of the anti-resorptive therapy.
  • another aspect of the present invention provides a method of predicting a treatment response of an individual after a therapy.
  • the present invention method comprises the steps of: a. measuring the baseline of a first variable comprising a biochemical marker in the individual at the beginning of therapy; b. monitoring the level of the first variable in the individual undergoing the therapy over a second variable; and c. deriving a probability of treatment response from at least the first order derivative of the first variable over the second variable and the first variable either at the baseline or at a time determined by the second variable.
  • One type of therapy is anti-resorptive therapy.
  • An example of the biochemical marker is serum bone alkaline phosphatase (BAP).
  • BAP serum bone alkaline phosphatase
  • the second variable may be time duration or other varying factors such as dosage of medicine, etc.
  • the first order derivative of the serum BAP level is simply the change of the serum BAP level over a period of time.
  • the first variable may be the BAP level at the baseline.
  • the first variable may be the BAP level at the time the change is determined.
  • the present invention provides a new model using the logistic combination of both the actual value and the percentage change of a bone marker after a short-term treatment period to identify patients who will subsequently demonstrate a positive bone mass response.
  • This model provides a higher diagnostic specificity and sensitivity than the two individual parameters used alone. Using this model, non-responders (or patients who do not comply with the treatment) can easily be identified using a simple two-scale graph.
  • the present invention also employs a cutoff based on a logistic regression model, including both percentage change of a bone marker from the baseline level and the bone marker level at a predetermined time. This combination allows a substantial increase in the sensitivity of predicting a positive bone mass response with a similar specificity when the " two parameters are used alone.
  • the methods of the present invention are well suited for use during the anti-resorptive treatment for assessing the treatment response and the compliance of the treatment. They may be applied to different markers of bone formation and bone resorption with different characteristics in terms of reproducibility and responses to treatment. Furthermore, the methods of the present invention may also be applied to a variety of anti-resorptive therapies, such as estrogen or alendronate treatment. Finally, the methods of the present invention may be used to test if the combination of different markers, such as one of formation and one of resorption, could improve the predictive performance as it has been suggested for the estimation of the spontaneous rate of bone loss (1 , 15, 16). The following examples illustrate one detailed implementation of the present invention method.
  • Lumbar spine BMD was determined by DXA at the baseline and at twenty-four months of treatment, using a Hologic QDR-1000 densitometer (Hologic, Waltham, USA). The change of BMD with time was expressed in percentage change from the baseline. The study was approved by the ethics committees of the participating medical centers.
  • Serum BAP Serum Bone Alkaline Phosphatase
  • the premenopausal range was established in one hundred thirty-four (134) healthy premenopausal (mean age: 41 ⁇ 5 years) women, belonging to a prospective population-based cohort (OFELY study: 1039 healthy volunteers, 31-89 years).
  • the premenopausal range was 8.7 ⁇ 2.7 mg/L (2).
  • responders were defined as women demonstrating a percentage BMD change of 3% or more after two years.
  • Non-responders to treatment were considered as patients with a two-year BMD change lower than 3%.
  • Maximum likelihood estimation and Chi-square tests were used to estimate the goodness of fit of the overall model, and the statistical significance levels of each parameter estimated in the logistic equation were computed.
  • the logistic regression model was considered appropriate only when the probability (p)-levels associated with Chi-square and the slopes of each variable in the logistic equation were statistically significant (p ⁇ 0.05).
  • Predicted probabilities (p) from the logistic regression were used to distinguish positivity (BMD response or patient in the alendronate group) from negativity (non-BMD response or patient in the placebo group). Cutoffs which provide appropriate sensitivity and specificity were established by ROC curve analysis.
  • Equation [3] indicates that for any particular t value, the cutoff corresponds to a straight line when patients' bone marker date is reported in two-dimension scatter-plots where axes represent the two variables, BAP percentage change from baseline (X) and absolute BAP level at either the baseline or at six months (Y). This straight line separates positive from negative data points with a sensitivity and specificity that are set when the cutoff value t is selected by ROC curve analysis.
  • Comparison of the Different Models of Prediction The model based on logistic regression analysis which combines both BAP level at either the baseline or six months and BAP percentage change at six months was compared with models using either one of these two individual discriminants. Overall discriminant performances were compared in terms of area under the ROC curve. Comparisons were also performed for a given threshold of specificity by paired Chi-square tests.
  • FIG. 1 shows a response of bone alkaline phosphatase to treatment with alendronate (10 mg/day) or placebo in 307 elderly osteoporotic women. Data are the mean ⁇ l SEM. After six months of alendronate treatment, 95% of values were within the premenopausal range.
  • FIG. 2 shows the relationship between the percentage change from the baseline after six months of treatment of BAP, BAP level after six months of treatment, and the predicted probability of lumbar BMD positive response (after twenty-four months of treatment) as computed by logistic regression in 307 elderly osteoporotic women. Responders are identified as patients with a percent change in lumbar BMD from baseline after twenty-four months of treatment at or greater than 3%.
  • the two latter groups (i.e., no BMD change and non-responders) have a similar distribution of BAP levels at six months and BAP percentage change at six months, and could not be discriminated by these two parameters.
  • these two groups were combined in the subsequent analyses, and the value of BAP levels, BAP percentage change at six months and the logistic combination of these two parameters were investigated to discriminate BMD responders (increase in BMD >3%).
  • Figure 5 shows the relationship between the percentage change from the baseline after six months of treatment of BAP, BAP levels at the baseline, and the predicted probability of lumbar BMD positive response (after twenty-four months of treatment) as computed by logistic regression in 307 elderly osteoporotic women.
  • responders are identified as patients with a percent change in lumbar BMD from baseline after twenty-four months of treatment at or greater than 3%.
  • the two latter groups i.e., no BMD change and non-responders
  • these two groups were combined in the subsequent analyses, and the value of BAP levels at the baseline, BAP percentage change at six months and the logistic combination of these two parameters were investigated to discriminate BMD responders (increase in BMD > 3%).
  • ROC curve for the prediction of BMD (Panel A) and alendronate-treated patients (vs. placebo, panel B) in 307 elderly osteoporotic women.
  • Three predictive models were compared: BAP percent change from baseline, BAP level, and their combination by logistic regression.
  • ROC curves were established for the three discriminants and the areas under the curves computed as a function of the treatment monitoring time. P-values refer to the significance level of the difference between those areas as indicated.
  • the discrimination between BMD responders/non-responders (Fig. 3A) and alendronate-treated patients/placebo (Fig. 3B) increases with time after initiating therapy, a plateau being reached at six months for the prediction of BMD response.
  • BAP percentage change from the baseline and the BAP level appear to be equivalent when used separately, except at three months when BAP level shows the lowest discrimination power. Irrespective of monitoring time, the discrimination power provided by logistic combination of BAP percentage change from baseline and BAP level is superior to that obtained with either of the two monitoring parameters taken separately.
  • a cutoff can be selected by logistic regression and ROC curve analysis (Figs. 4 and 6) to provide after six months of treatment a specificity of 90%.
  • Figure 4 shows the prediction of lumbar spine BMD response to twenty-four months of treatment, based on either BAP percent change (from baseline after six months of treatment) or BAP level (after six months of treatment) or their combination by logistic regression in 307 elderly osteoporotic women.
  • Discriminant cutoff values were selected to provide 90% specificity for non- response prediction for each model, so that resulting sensitivities for positive response prediction could be compared.
  • BMD responders, no BMD change and BMD non-responders were defined as in Figure 2. Sensitivity and specificity figures apply for the discrimination of BMD responders from BMD non- responders and no BMD change considered as a single group.
  • This cutoff implies that no more than 10% of women classified with markers as having a subsequent positive BMD response (i.e., an increase at two years > 3%) would be false positive. This cutoff results in a 72% sensitivity in the detection of patients who will demonstrate a favorable spine BMD increase after two years of treatment.
  • Figure 6 shows the prediction of lumbar spine BMD response to twenty- four months of treatment, based on either BAP percent change (form baseline after six months of treatment) or BAP level (at the baseline) or their combination by logistic regression in 307 elderly osteoporotic women. Likewise, discriminant cutoff values were selected to provide 90% specificity for non-response prediction for each model, so that resulting sensitivities for positive response prediction could be compared. BMD responders, no BMD change and BMD non-responders were defined as in Figure 2. Sensitivity and specificity figures apply for the discrimination of BMD responders from BMD non-responders and no BMD change considered as a single group.
  • Table 1 shows the sensitivity of different models to predict two-year lumbar spine BMD response or to distinguish placebo from alendronate-treated patients for a given 90% specificity in each model.
  • BMD response can be predicted with a 93% specificity which is comparable to the 90% of the logistic model response at two years but with only 54% of sensitivity, which is significantly lower than the 72% obtained from the logistic combination (p ⁇ 0.002).
  • the least significant change cutoff is less powerful to differentiate placebo from alendronate- treated patients than the combination model with a 22% lower sensitivity (60% vs. 82%, p ⁇ 0.001 ) for a similar specificity (95% vs. 90%, NS).
  • BAP least significant change calculated from the placebo group overestimates BAP variability in normal individuals.
  • a value of 25% has been proposed as a cutoff of significant change of BAP (13).
  • a cut-point of -25% BAP percentage change from baseline at six months would result in sensitivities for treated individuals or for positive BMD response prediction statistically similar to those observed with the logistic (85% vs. 82% and 78% vs. 72%, respectively).
  • resulting specificity would be much lower than that obtained from the logistic combination: 78% and 75% versus 90%, respectively (p ⁇ 0.001 ).
  • the present invention provides a new model using the logistic combination of both an absolute level and the percentage change of a bone marker after a short-term treatment period to rapidly identify patients who will subsequently demonstrate positive BMD responses.
  • This model gives higher diagnostic specificity and sensitivity than the two individual parameters considered alone.
  • non-responders or patients who do not adequately take alendronate
  • the present invention is based on the discovery that the combination of both the absolute level of BAP and the percentage change of BAP at the six- month date of an anti-resorptive therapy can be used to predict the probability of a positive BMD response after two years of the therapy, which responses is determinative of the effectiveness of the treatment.
  • the present invention combines these two parameters, i.e., percentage BAP change and BAP level, to improve the prediction of bone mass response.
  • the present invention combines these two parameters, i.e., BAP level at the baseline and percentage BAP change at six months to improve the prediction of bone mass response.
  • a cutoff based only on the percentage change from baseline of bone marker levels after thee to six months of treatment i.e., when the decrease of bone turnover has reached a plateau, may not be sufficient to accurately identify patients who will respond favorably to treatment in terms of BMD gain after two years. Indeed, it seems reasonable to consider that patients who have relatively low bone marker levels before treatment may only demonstrate a slight decrease in bone marker and might not be identified as responders despite levels at six months within the normal range, demonstrating the efficacy of the treatment to normalize bone turnover. Therefore, a parameter which represents the level of bone turnover at either the baseline or at a level reached after treatment might be an additional and independent predictor of BMD response.
  • the present invention performed logistic regression, including both percentage change from baseline and BAP level at either the baseline or at six months to predict BMD responders. It is found that these two parameters are significant and independent predictors of BMD response and their combination provides a significantly higher predictive value than each parameter alone. For example, a high specificity, i.e., the proportion of non-responders who are identified by the predictive model at six months and who indeed did not demonstrate a significant gain in spine BMD at two years, is likely to be the most relevant option that could lead to therapeutic adjustment, including changing drug or dose.
  • This model may be generaiizable to other populations.
  • this model was constructed from predicted probabilities which are as any predictive value dependent on the prevalence of positive cases in the study population used for their computation by logistic regression, the deduced cutoff lines used to differentiate between positive (BMD responder) and negative (non-responder) cases with defined sensitivity and specificity characteristics are independent of any prevalence.
  • these cutoff lines determined in this large population are likely to be useful to provide corresponding sensitivity and specificity values in any population with comparable properties in terms of BAP response, even if expected prevalence of positive cases are different.
  • this predictive model may be more difficult to use than a single percentage BAP change parameter because it requires combining two parameters.
  • non-responders can easily be distinguished from responders by reporting patient characteristics (% BAP change at six months and BAP level at six months or at the baseline) in a two-scale graph with the cutoff as a straight line.
  • This model may be applicable to other markers of bone formation and bone resorption with different characteristics in terms of reproducibility and response and to other anti-resorptive therapies such as estrogen.
  • This model could also be used to test if the combination of different markers, such as one of formation and one of resorption, could improve the predictive performance as it has been suggested for the estimation of the spontaneous rate of bone loss (14, 15).

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Abstract

La présente invention concerne un nouveau modèle basé sur la combinaison logistique du changement en pourcentage du niveau d'un marqueur osseux à une période de temps prédéterminée, ainsi que le niveau du marqueur osseux à une ligne de base ou à la période prédéterminée afin de prévoir peu après le début d'une thérapie anti-résorption quels patients n'amélioreront pas sensiblement leur densité minérale de l'os après deux ans de traitement, ou quels patients ne sont pas adaptés à la thérapie. De plus, la présente invention concerne également, pour la première fois, une valeur de seuil définie pouvant être utilisée facilement en pratique clinique pour identifier les patients individuels non répondeurs ou non adaptés après une courte durée de traitement, par exemple six mois.
PCT/US1999/020698 1998-10-14 1999-09-09 Methode de pronostic pour traitement anti-resorption Ceased WO2000022437A1 (fr)

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