Clinical Data (CLDA) 8-K Clinical Data Announces Presentation

EXHIBIT 99.2

Pharmacogenomics: Hope & Reality

Israel M. Stein MD
President , CEO
January 19, 2006

Clinical Data^®

Forward-Looking Statement

SAFE HARBOR STATEMENT UNDER THE PRIVATE SECURITIES LITIGATION REFORM ACT OF 1995

The following presentation contains certain forward-looking information about Clinical Data that are intended to be covered by
the safe harbor for “forward-looking statements” provided by the Private Securities Litigation Reform Act of 1995.  Forward-
looking statements are statements that are not historical facts. Words such as “expect(s)”, “feel(s)”, “believe(s)”, “will”, “may”,
“anticipate(s)” and similar expressions are intended to identify forward-looking statements.  These statements include, but are
not limited to statements regarding: our ability to successfully integrate the operations, business and technology obtained in
our recent acquisition of Genaissance Pharmaceuticals and to be obtained in our announced acquisition of Icoria;  our ability to
obtain regulatory approval for, and successfully introduce our new products; our ability to expand our long-term business
opportunities; our ability to maintain normal terms with our customers and partners; financial projections and estimates and
their underlying assumptions; and statements regarding future performance.  All of such statements are subject to certain risks
and uncertainties, many of which are difficult to predict and generally beyond the control of the Company, that could cause
actual results to differ materially from those expressed in, or implied or projected by, the forward-looking information and
statements.  These risks and uncertainties include, but are not limited to:  our ability to consummate the acquisition of Icoria
and to achieve the expected synergies and operating efficiencies from the Genaissance acquisition and the Icoria acquisition;
the strength of our intellectual property rights; competition from pharmaceutical, biotechnology and diagnostics companies; the
development of and our ability to take advantage of the market for biomarker and pharmacogenomic products and services;
general economic downturns; and other risks contained in our various SEC reports and filings, including but not limited to our
Annual Report on Form 10-KSB for the fiscal year ended March 31, 2005, and our 2004 and 2005 quarterly Form 10-QSB and
Form 10Q filings.  Our audience is cautioned not to place undue reliance on these forward-looking statements that speak only
as of the date hereof, and we do not undertake any obligation to revise and disseminate forward-looking statements to reflect
events or circumstances after the date hereof or to reflect the occurrence of or non-occurrence of any events.

Pharmacogenomics                                HOPE

Developing a new generation of therapeutic products that are
efficacious and safe for populations that are defined by a genetic
diagnostic test.

Individualized drug therapies

Match a patient with effective and safe medications based on
genetic diagnostic tests.

Personalized disease prevention strategies

Develop using genetic tests that estimate a patient's risk of getting
a particular disease, combined with individualized therapies.

Biomarkers Change the
Drug Response Paradigm

Efficacy/safety is enriched through patient selection

Drug Response

Conventional
Drugs

PGx/Rx
Products

Potential Benefit

Diagnosis

Drug 2

Drug 3

Drug 4

Drug 1

Time

Reactive Medical Care

Diagnosis

PGx Drug 1

Time

Predictive Medical Care

Pharmacogenomics

Guide clinical drug utilization to

Reduce adverse events

Improve patient outcomes

Reduce cost

Speed drug development

Permit retrospective analyses of adverse events

How Can Pharmacogenomics Contribute?

Biomarkers to identify those who will get the best response to a drug

Drug Class

Indication

% Responders

Beta-blockers

Heart disease, HTN

65 - 85%

ACE-inhibitors

High blood pressure

63 - 86%

AGT2R inhibitors

High blood pressure

71 - 88%

HMGCoA reductase inhibitors

Cholesterol

66 - 89%

SSRI

Depression

65 - 91%

Tricyclic Antidepressants

Depression

43 - 80%

Anticancer

Cancer

20 - 80%

Adverse Drug Reactions

5% of all hospital admissions (1.5 million cases)

106,000 deaths in USA annually

2.2 million patients suffer serious, but not deadly, side
effects.

Increased length of hospital stay

Increased cost per patient - $2500

ADR associated costs $177 billion

Pharmacogenomic safety biomarkers could identify patients
most likely to develop serious adverse drug reactions

How Can Pharmacogenomics Contribute?

Source: Kromite Consulting; 5 yr averages

Discovery

Pre-Clinical

Development

Phase I

Phase II

Phase III

Approval

  65%

35%

  14%

  12%

Probability of
success

Recapture stalled drugs due to efficacy and/or
safety issues

Regulators and Industry Support

FDA defining the regulatory terrain:

Guidance for Pharmacogenomic Data Submissions issued May
2005

Seen as a key tool in streamlining the drug development process
and is featured prominently in the FDA Critical Path

Builds on early examples:  Herceptin, Gleevec and Erbitux

Government and other payors strongly supportive

Growing acceptance by the industry

Growing acknowledgment that the ‘blockbuster’ model is broken

Pace of collaborations and investment increasing

Recently acquired Genaissance Pharmaceuticals and Icoria

475 employees worldwide

Annualized Revenues of ~$100 mln

NASDAQ National Market      CLDA

ISO 9001, EN13485, CE, FDA, GLP, CLIA

Clinical Data^®

To become a leader in commercializing
pharmacogenomics and clinical diagnostics

Our strategic focus is to develop diagnostics
that determine how a person's genetic makeup
effects drug efficacy and safety

Clinical Data^®

Mission & Strategy

TPMT Test for
Drug Toxicity

PGx/Rx
Vilazodone

Pipeline                                             Reality

Clozapine PGx

Marketed

Marketed

Marketed

Expand sales

Initiate Phase II

Plan PGx/Rx trial

2005

2006

Commence Marketing

Validation study

Pharmacogenomics
Two CNS Drugs                                         Reality

Vilazodone

anti-depressive

Efficacy Biomarker

Clozapine

anti-psychotic

Safety Biomarker

Vilazodone                Pharmacogenomics

Reposition a stalled compound by developing efficacy
biomarkers

Apply these biomarkers in subsequent trials to

enrich a study with likely responders

demonstrate efficacy of the drug

Identify biomarkers that may be used in proving
efficacy for a similar class of drugs in future studies

Vilazodone

Primary Indication: Treatment of depression

Profile: New generation of a dual serotonergic
antidepressant

Mechanism of Action:

Selective Serotonin Reuptake Inhibitor (SSRI)

5HT1A partial agonist

Development Stage:  Phase II

Ineffective Therapies Lead to Physician and
Patient Dissatisfaction

Unsatisfactory results for 50% of patients with
first line drugs

Discontinuation rates and resulting relapses very high

Poor differentiation of therapeutic benefit for currently
marketed SSRIs

Physicians are likely to adopt a test/drug combination
product with improved efficacy

A test/drug combination product would be “novel, unique and add
science or reason to prescribe the therapy”

Genetic Rationale for Finding Efficacy Biomarkers

Depression is a complex genetic disease

Response to SSRIs has a strong genetic component

Patients who fail to respond to one SSRI will respond to another

A given individual’s response predicts the same outcome in first-
degree relatives

A genetic marker for response to SSRIs has been validated

Indel in promoter of gene that encodes the serotonin transporter
protein is associated with response

Genetics of Response to SSRIs
Previous Associations

Source: Reviewed in Serretti, The Pharmacogenetics Journal 2004; 233-44

Gene

Allele(s)

Drug(s)

Endpoint(s)

Fluvoxamine, Paroxetine

probability of response

Fluvoxamine, Paroxetine

time to response

Citalopram

remission

Fluvoxamine

better response

Tryptophan Hydroxylase

(TPH)

A218C

Paroxetine

time to response

G Protein Beta 3 (GNB3)

C825T

Fluvoxamine, Paroxetine

response

Serotonin Transporter

(SLC6A4)

Long and Short

(promoter Indel)

Serotonin Transporter Variability and Response to
SSRIs in Caucasian Populations

Study

Drug

p-value

Smeraldi, 1998

Fluvoxamine

6

<0.0001

(SS vs. LS/LL)

Pollock, 2000

Paroxetine

2

<0.0001

(LL vs LS/SS)

Zanardi, 2000

Paroxetine

4

0.0001

(SS vs LS/LL)

Rausch, 2002

Fluoxetine

12

0.02

(SS vs. LS/LL)

Promoter region insertion/deletion; marker
frequency in Caucasians

Follow-up

(Weeks)

SS24%   LS45%   LL31%

Vilazodone: A “Stalled” Compound Ideal for
Pharmacogenetic Rescue

Pre-clinical data suggest vilazodone will be a potent
antidepressant with a novel mechanism of action

Pre-Clinical Toxicity is acceptable

Large amount of safety data show safety is acceptable for phase
of development and similar to SSRIs

Failure to demonstrate efficacy in unselected population but
scientific basis exists to genetically identify responders

Vilazodone: A “Stalled” Compound Ideal for
Pharmacogenetic Rescue

Utilize Genaissance’s HAP Database and expertise in biomarker
development

Diagnostic test/drug combination answers unmet medical need

Pharmacogenetics can position vilazodone for successful development
and as a clearly differentiated antidepressant

Clozapine

Develop safety biomarkers for a rare but serious
adverse drug reaction

Biomarker would have immediate utility as a risk
assessment tool

May enable a highly effective drug to reach a broader
population

Clozapine Induced Agranulocytosis  (CIA)

Defined as an absolute Neutrophil count of <500/mm³

Incidence of 1.3% without monitoring

Incidence of 0.3%-0.4% with weekly monitoring

Progression from reduction in absolute neutrophil
count to agranulocytosis is ~2-5days

Recovery takes 14-24 days

Rationale for Genetic Markers for CIA

Clozapine is considered by many to have superior
efficacy in treating schizophrenia

The only drug shown to be superior to standard treatment
in a randomized controlled study

Rationale for Genetic Markers for CIA

Restricted to third-line therapy due to the risk of CIA

In U.S., ~ 2.5 million patients with schizophrenia take
antipsychotics

~80% are on first-line therapy; ~16% on second-line therapy

Clozapine is confined to about 4% of the market

“No blood, no drug” policy limits utility

Evidence for Genetic Component to CIA

A disproportionate number of the U.S. cases occurred
in patients of similar ethnic background

Inter-individual variability in the onset of CIA

Published evidence for genetic associations

Select Published Associations with CIA

5.95

0.01

19

25

HLA-DRB1/B5¹

2.85

0.02

77

31

HLA-C²

0.04

<0.001

Raw
p-value

5.30

>6

OR

49

18

N
Cases

78

80

N
Controls

MPO⁴

NQO2³

Gene

¹ Yunis, et al. Blood 1995

² Dettling, et al. Pharmacogenetics 2001

³ Ostrousky, et al. Tissue Antigens 2003

⁴ Mosyagin, et al. J Clin Psychopharm 2004

Goal

Discover a marker that is

Found in at least 80% of patients who develop CIA

Found in only 20% of patients who do not develop CIA

Such a marker would lead to an ~80% reduction in CIA

Candidate Genes

74 candidate genes (1219 polymorphisms)

Genes involved in clozapine metabolism

Genes involved in promyelocytic differentiation

Genes for which associations with CIA have been discovered

Apply proprietary HAP Database content and haplotype analysis

103

major histocompatibility complex, class I, C

HLA-C

21

tumor necrosis factor

TNF

17

heat shock 70kDa protein 1A

HSPA1A

MHC and Linked to
the MHC

50

neutrophil cytosolic factor

NCF4

Metabolism

53

myeloperoxidase

MPO

37

cytochrome P450, family 3, subfamily A,
polypeptide 4

CYP3A4

# Polymorphisms

Gene Symbol

Gene Symbol

Category

CIA Associations     (33 Cases, 54 Controls)

5.7%

0.27%

94%

36%

11.8

0.0079

0.00065

NTSR1

0.11%

0.12%

0.33%

0.25%

0.16%

0.06%

100 – NPV*

20.7%

37.2%

0.1%

3.9%

16.9%

39.1%

% Test
Positive

0.0000010

0.000051

0.0022**

0.00012

0.000013

0.000049

Raw
p-value

****

0.032

0.010

0.0040

0.010

0.0050

Perm.    p-
value

20.9

12.8

***

26.8

12.9

16.1

Adj.
OR

63%

82%

HLA-C

80%

79%

HLA-DQB1 M2
and X

100%

18%

DRD1

96%

39%

Gene X

83%

67%

HLA-DQB1 M2

61%

91%

HLA-DQB1 M1

Spec.

Sens.

Gene(s)

*    Assuming 0.4% incidence

**   From Fisher’s exact test

***  Not defined (zero-cell)

**** Not applicable

Pharmacogenomics:
Hope & Reality

Israel M. Stein MD
President , CEO

Clinical Data^®