Improving the specificity and precision of PANSS factors:

One approach to facilitate development of novel treatments in schizophrenia

Seth C. Hopkins, PhD Executive Director Translational Medicine Sunovion Pharmaceuticals

1 Use of Clinical Trial Databases to Improve RCT Efficiency • Industry-sponsored RCTs generate large clinical databases – which can be better examined to advance the field and improve public health

volume of clinical observations • different compounds • range of doses

tens of thousands of unique research subjects • acute studies • long-term safety studies

2 How to apply clinical trial databases to facilitate drug development, including drugs with novel mechanisms of action? • The potential for clinically-meaningful differentiation is an incentive for innovation, especially for a novel MoA

subsub- domains more accurate/specific descriptions of treatment effect across sub-domains subsub- of schizophrenia populations symptoms? better understanding of effects in stable/specific patient types?

3 Historical view of the accumulated database Cumulative druglaunches(world-wide)forschizophrenia in schizophrenia drug development Cariprazine Brexpiprazole Lurasidone Iloperidone Asenapine Drug launches for schizophrenia Blonanserin Paliperidone Sertindole Aripiprazole Perospirone Ziprasidone Quetiapine Olanzapine Risperidone Emonapride Levosulpiride Amisulpride Zotepine Bromperidol Carpipramine Fluphenazine Clozapine Pimozide Spiperone Sulpiride Thiothixene Clopenthixol Haloperidol Thioridazine Promazine Chlorpromazine Serotonin 5-HT2A Dopamine D2

0 0 0 80 90 195 196 197 19 19 2000 2010 2020 4 Thompson Reuters | Integrity Database year of launch The cumulative number of research subjects is large

Dopamine D2 based mechanism

cumulative number of schizophrenia subjects enrolled in RCTs for industry-sponsored drug development programs (ClinTrials.org) (cumulative) Subjects enrolled Subjects

ClinTrials.org industry-sponsored | schizophrenia | phase 2 or phase 3 | randomized, placebo-controlled | primary endpoint efficacy 5 Total investment in clinical development for new schizophrenia compounds is large

Dopamine D2 based mechanism (cumulative) Subjects enrolled Subjects all others

drug-development RCTs in schizophrenia (start dates)

6 ClinTrials.org | industry-sponsored | schizophrenia | phase 2 or phase 3 |randomized, placebo-controlled Total investment in compounds with novel mechanisms is also large

Dopamine D2 based mechanism non-D2 mechanisms (cumulative) Subjects enrolled Subjects all others all others

7 ClinTrials.org | industry-sponsored | schizophrenia | phase 2 or phase 3 |randomized, placebo-controlled The success of compounds with non-D2 MoA’s has been poor relative to D2 compounds

Dopamine D2 based mechanism non-D2 mechanisms

launches (cumulative) Subjects enrolled Subjects all others all others

8 ClinTrials.org | industry-sponsored | schizophrenia | phase 2 or phase 3 |randomized, placebo-controlled Development of compounds with non-D2 MoA’s has focused on unmet needs of Cognition and Negative Symptoms

Dopamine D2 based mechanism non-D2 mechanisms

launches (cumulative) Subjects enrolled Subjects all others all others

Cognition Relapse PANSS total PANSS total

Primary Endpoints as portion of total enrolled subjects Negative Symptoms Relapse

Drug development RCTs for new treatments in schizophrenia have incorporated new measurements of specific symptom domains 9 ClinTrials.org | industry-sponsored | schizophrenia | phase 2 or phase 3 |randomized, placebo-controlled How to apply clinical trial databases to facilitate new treatments, including drugs with novel MoA’s?

Dopamine D2 based mechanism non-D2 mechanisms Cognition Relapse PANSS total PANSS total

Negative Symptoms Relapse

RDOC MATRICS SCALES

IMPROVE MEASUREMENTS

ACADEMIA PHARMA NIMH/NIH Use the existing PANSS scale, to measure, with Drug development RCTs for new treatments in enhanced specificity, treatment effects across schizophrenia have incorporated new key symptom domains of schizophrenia measurements of specified symptom domains 10 PANSS factors are correlated Can we transform them to be more specific? N=1,710 Sunovion dataset acute schizophrenia, 6-weeks {lurasidone, placebo, olanzapine, quietiapine} factor analysis of change-from item scores retain score matrix coefficients

uncorrelatedUPSM UPSM factorscores scores domain A

domain B • (ORTHOGONAL) represent symptom change specific to each dimension

• (FACE VALID) corresponds to recognizable/established dimensions

• (APPLICABLE) UPSM can transform external PANSS data sets Uncorrelated PANSS Score Matrix (UPSM) 11 Pseudospecificity: A major confound in the assessment of efficacy in schizophrenia Correlations Among Marder PANSS Factor Scores (Week 6 Change from Baseline) SUNOVION DATA (N=1,710 patients from 5 placebo-controlled schizophrenia trials) Marder PANSS factor pos dis neg hos/exc anx/dep tot Positive Symptoms 1 Disorganized Thought 0.74 1 Negative Symptoms 0.57 0.62 1 Hostility/Excitement 0.64 0.59 0.43 1 Anxiety/Depression 0.52 0.45 0.40 0.46 1 PANSS Total 0.90 0.86 0.77 0.77 0.66 1

OTSUKA DATA (N=1,368 patients from 5 placebo-controlled schizophrenia trials) Marder PANSS factor pos dis neg hos/exc anx/dep tot Positive Symptoms 1 Disorganized Thought 0.73 1 Negative Symptoms 0.59 0.67 1 Hostility/Excitement 0.64 0.58 0.42 1 Anxiety/Depression 0.58 0.51 0.49 0.48 1 PANSS Total 0.89 0.87 0.81 0.75 0.71 1

TAKEDA DATA (N = 240 patients from 1 placebo-controlled schizophrenia trials 20 mg TK-063) Marder PANSS factor pos dis neg hos/exc anx/dep tot Positive Symptoms 1 Disorganized Thought 0.65 1 Negative Symptoms 0.46 0.50 1 Hostility/Excitement 0.46 0.41 0.23 1 Anxiety/Depression 0.55 0.50 0.45 0.47 1 PANSS Total 0.85 0.80 0.71 0.66 0.76 1 12 UPSM transform reduces between‐factor correlations in endpoint PANSS factor change scores SUNOVION DATA (N=1,710 patients from 5 placebo-controlled schizophrenia trials) Transformed PANSS factors POS DIS NAA NDE HOS ANX DEP POSITIVE 1 DISORGANIZED 0.20 1 NEG APATHY/AVOLITION 0.10 0.08 1 NEG DEFICIT OF EXPRESSION 0.04 0.12 0.22 1 HOSTILITY 0.21 0.12 0.07 -0.02 1 ANXIETY 0.09 0.04 -0.01 -0.08 0.13 1 DEPRESSION 0.10 0.00 0.12 0.13 0.04 0.27 1 PANSS TOTAL SCORE 0.60 0.45 0.45 0.36 0.53 0.45 0.46

OTSUKA DATA (N=1,368 patients from 5 placebo-controlled schizophrenia trials) Transformed PANSS factors POS DIS NAA NDE HOS ANX DEP POSITIVE 1 DISORGANIZED 0.17 1 NEG APATHY/AVOLITION 0.12 0.12 1 NEG DEFICIT OF EXPRESSION 0.03 0.22 0.37 1 HOSTILITY 0.08 0.18 0.12 -0.04 1 ANXIETY 0.06 0.12 0.05 -0.01 0.16 1 DEPRESSION 0.21 0.08 0.26 0.28 0.07 0.27 1 PANSS TOTAL SCORE 0.47 0.52 0.57 0.48 0.49 0.46 0.57 TAKEDA DATA (N = 240 patients from 1 placebo-controlled schizophrenia trials 20 mg TK-063) Transformed PANSS factors POS DIS NAA NDE HOS ANX DEP POSITIVE 1 DISORGANIZED 0.07 1 NEG APATHY/AVOLITION 0.18 -0.07 1 NEG DEFICIT OF EXPRESSION -0.02 -0.07 0.15 1 HOSTILITY -0.01 -0.13 -0.15 -0.02 1 ANXIETY 0.28 -0.01 0.24 -0.18 0.44 1 DEPRESSION 0.29 -0.16 0.29 -0.06 0.09 0.36 1 PANSS TOTAL SCORE 0.59 0.14 0.50 -0.23 0.38 0.61 0.65 13 Towards specificity in antipsychotic drug treatment effects N=5 acute schizophrenia trials PANSS change from baseline, at Week 6 endpoint, lurasidone active doses vs. placebo

MARDER PANSS FACTORS UPSM TRANSFORMED PANSS FACTORS overlapping drug effects more specificity sub‐ -0.38 POSITIVE -0.35 domains

Attributions of specific treatment effects greater heterogeneity -0.44 -0.19 confounded by DISORGANIZED in effect size correlations among estimates across the improvements in Marder sub-domains PANSS Factor Scores APATHY/AVOLITION -0.22 -0.32 NEGATIVE DEFICIT OF EXPRESSION -0.04

-0.36 HOSTILITY -0.27

-0.18 ANXIETY -0.33 DEPRESSION/ANXIETY -0.14 DEPRESSION

PANSS TOTAL -0.46 -0.45 TOTAL FACTOR SCORE

-0.9 -0.6 -0.3 0.0 -0.9 -0.6 -0.3 0.0 improvement worsening improvement worsening drug vs. placebo effect size ± 95%CI Hopkins et al. 2017 Schizophrenia Bulletin 14 Discussion Point: Treatment effects are separable among the dimensions of schizophrenia sub‐ domains

UPSM-transformation identified 2 PANSS-negative symptom sub-factors showing differential treatment effects, with larger effect sizes observed for the apathy/avolition sub-factor compared to the deficit of expression sub-factor

DRUG A DRUG B If symptom dimensions can be separately identified and assessed even in the acute schizophrenia clinical trial setting...

...then therapeutics with novel efficacy profiles across the sub- dimensions of schizophrenia psychopathology can be evaluated

15 Understanding of specificity within PANSS to advance the field and facilitate development of new treatments

• specificity among symptom sub-domains Forest subsub- Plots with populations specificity

Does an understanding of specificity of symptom Address issues of: change over time help to • differentiation • dose-response identify more-meaningful • benefit/risk patient types? Use during: • Objective of this analysis direction: drug development • decisions examine failed/negative trials • • test for differential treatment responses among types meta-analyses • develop “a priori” categories for future analysis plans of novel MoA’s more specific breakdown of total symptoms improvements according to the domains of most-prominent effects 16 Identified patient types at baseline who are prominent along specific UPSM factor scores

are patient-types stable? do they persist post-baseline?

subsub- populations post-baseline

PROMINENTLY POSITIVE track response of patient- types post-baseline

POS PATIENT TYPES baseline post-baseline HOSTILE PANSS PANSS transform (UPSM) DISORGANIZED transform (UPSM) train classifier apply classifier (SVM) (SVM) ANX DEP mean cluster UPSM (K-MEANS) factor PATIENT TYPES scores NEG DIS ANX DEP NAA POS NDE HOS BASELINE

17 Patient-types persist over time, even in the context of overall symptom (PANSS total) improvements Toward better definitions of patient-types within PANSS to better understand and characterize treatment response Objectives of this analysis • examine failed/negative trials • test for differential treatment responses among types • develop “a priori” categories for future analysis plans of novel MoA’s duration Patient types persist over time and over treatment POS PROMINENTLY POSITIVE PATIENT-TYPE PROMINENTLY POSITIVE

POS HOS

DIS HOS number of ANXDEP subjects

DIS N=200 N=400 NEG ANX DEP mean UPSM factor DISCONTINUATION scores NEG DIS

ANX DEP NAA 0123456 POS NDE HOS BASELINE SEQUENTIAL WEEKLY VISITS ENDPOINT N=1,710 subjects | 5 studies | acute schizophrenia RCTs 18 {lurasidone, placebo, olanzapine, quietiapine} Future directions in the use of clinical trial databases for improving RCT efficiency • An understanding of the specificity of symptom change in PANSS ... can facilitate the evaluation of therapeutics with novel efficacy profiles across the sub-dimensions of schizophrenia psychopathology

subsub- subsub- populations domains

Towards collaborative analyses and shared research questions for our existing clinical trial databases

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