webinar: new rmc - your lead_optimization solution june082017

Olivier Barberan

Senior Product Manager

New Reaxys Medicinal Chemistry: your lead

optimization solution

Key challenges in drug discovery and Lead

optimization

How NEW Reaxys Medicinal Chemistry supports Hit

to lead and Lead Optimization based with live

examples

Q&A

Summary

Productivity in pharmaceutical

development is at an all-time low

considering rising costs of R&D

Drop In FDA Approvals Rekindles

Fears For The Future Of Pharma:

2016 is a challenge!

Pharma companies are challenged to improve their R&D

outcomes

Target ID & Validation

Lead ID & Validation

Pre-clinical

Clinical(Phase I to III)

Post-Launch

Characterize &

understand

disease

Identify, design &

validate leads

Cull/prioritize

leadsDetermine safety

and efficacy profile

Manage risk &

compliance; improve

patient care

Source: Tufts Center for the study of drug development, Nov 2014

$125 M $773 M $200 M $1,460 M $3–5 B

Cost (/NME)

“We cannot fail for reasons we could have predicted.

We should fail only for reasons we could not predict.”

—Dr Moncef Slaoui

Head of Global R&D, GSK

• Low margin of safety is a major

cause of attrition in Phase I and II

• Lack of efficacy is a major cause

of attrition in Phase II and III

Better informed decisions at the Lead

ID & Validation stage generates

more optimized leads and mitigates

failures and miss-investments

80% 65% 69% 12%

Success Rate

Investing in earlier development stages builds up the

pipeline and reduces attrition from foreseeable causes

Deliver smarter lead compounds

Optimize efficacy and potency on

animal model disease

Deliver safer lead compounds

Higher chance of success rate in the

development process

High-

throughput

screening

Synthesis

of analogs

Improve

efficacy cell

assays

Improve

selectivityImprove

affinity on

“on target”

Optimize

metabolism

Optimize

Pharmacokinetic

Optimize

Cell

penetration

New analogs

improved

potency

Reduced

off-target

activities

Optimize

efficacy on

Animal M.

Decrease In

vitro Toxicity

Hit/lead

Optimization

What are chemists in hit to lead identification trying to

achieve?

Potency &

selectivity

DMPK properties

Physical properties

Safety

pharmacology

The lead optimization challenge: Optimization of early

substance to potential drug

Substances

Chemical structure ,Name, code, synonym of compound, calculated

physchem properties (log P, HBA, HBD, PSA, RotB), Lipinski rules of 5

Druggable target

Explore Target affinity patterns of chemical

compounds

In vitro and Cell Based assays

In vitro assays (binding, second messenger etc..) and Cell based assays for

example : Aggregation, Angiogenesis, Apoptosis, Cell differentiation, etc…

Animal models disease

Zucker rats for obesity model, ovariectomized rat in osteoporosis, treatment

of glaucoma, Xenografted animals with tumors to test antineplastic drugs

Pharmacokinetic and ADME Properties

Metabolic stability, Intrinsic clearance, Half life of elimination, Bioavailability,

In vivo Clearance

Toxicity

Cytotoxicity, cardiotoxicity, chronic

toxicity

Reaxys Medicinal Chemistry coverage

“The power of Reaxys Medicinal Chemistry is that the data are ready

to be discovered, used, digested and analyzed.

That laborious work of preparing the data is done. The user can now

focus on gaining insights.”

• millions of data points in

Reaxys Medicinal Chemistry

can serve as direct input for

any desired analysis.

• The pX value featured in the

database is a standardized

measure of affinity.

• The heatmap in the Reaxys

Medicinal Chemistry user

interface capitalizes on this

comparability to provide an

interactive matrix that

summarizes affinity for a

large number of compound–

target pairings and can be

used to explore factors that

contribute to affinity or find

interesting activity hotspots

Parameter Filter

Normalization of bioactivites pX Concept?

Parameter Grinder

IC50, Ki, % Inhibition, %,EC50, pKi, ED50, pIC50, AUC, Emax(%),

Concentration, Cmax, nH, pA2, % Stimulation, Tmax, Fold

increase, t1/2 el, Rate, Number, Kd, pEC50, pKb, IA (%), Time, Km,

ID50, Delta, Vmax, Cl, Clint, Ue(%), pD2, %max, Kb, Bmax, Cavg,

Pressure, Amount, t1/2, Cl/F, Cmin, MED, fu, F(%), Dose, ClR,

AUC i/AUC, LD50, Frequency

PARAMETERS RELATED TO CONCENTRATION

pIC50, pEC50, pED50, pID50, pLC50, pLD50, pKi, pKd, pKb, pGI, pD2,

pD’2, pA2 , IC50, IC20, IC80, EC50, ED50, ID50, LC50, LD50, CC50, CD50,

CIC50, CID50, GI, MBC, MCC, MEC, MED, MFC, MIC, TGI, Ki, Kd,

Kb, Ka, Ke , Km,Kapp,Kic, Kiu, % Inhibition





pX is computed

Filter value for concentration

based parameters

Normalization to a single

comparable metric

Original values are preserved; this is an additional computed descriptor

Computation of pX value: - log (Affinity) and affinity

results

Like pIC50, pEC50, pED50, pID50, pLC50, pLD50, pKi, pKd, pKb, pD2, pD’2, pA2

pX = pIC50 etc….

Remark

If values are expressed in Weight/voluem( like g/l), they are first converted in M (using molecular

weight, animal/tissue weight or volume)

Results are expressed as –log10 (affinity)

IC50, EC50, ED50, ID50, LC50, LD50, Ki, Kd, Kb, Ka, Ke

pX= -log10(IC50)

Results are expressed as affinity

Hit to lead : Virtual screening

Ligand Based virtual Screening – Using Reaxys

Medicinal Chemistry

Objective

• Describe an In Silico Screening approach

using Reaxys Medicinal Chemistry

Case Study on T-Type calcium channels

Ligand-Based In Silico Screening

Filter on active

compound pX>7

ANSWERS

730 compounds

Simple Target name

search returns all

results

Ligand-Based In Silico Screening

730 Query structures

Representation & Chemical Space Molecular descriptors & Fingerprints

Virtual Screening Pharmacophoric Similarity

N

O

N

NN

O

N

N

N

314 Hits

"Drug-like" Filtering

1. Molecular diversity and chemical originality

2. Compounds availability

39 compounds ordered for testing

28 M Substances

Chemical space based on

Synthesized substances

Biological activity

Electrophysiology experiments: Screening @10 µM on Cav3.2 T-Type channels

9 compounds with a % inhibition > 75%

15 compounds with a % inhibition >50%

Lead Optimization

Potency &

Selectivity

DMPK Properties

Physical properties

Safety

pharmacology

Lead optimization

ADMET Properties influencing medicinal

Chemistry design

• logD7.4

• Protein Binding

• Solubility

• Metabolic

Stability

• hERG

• Etc…

Step1

•Rat PPB

• Hu heps

• CYP inhib

• Caco2

• NaV1.5

• Etc…

Step2

• logD7.4

• Solubility

•Protein Binding

• hERG

• Rat PPB

• Metabolic

Stability

• CYP inhib

• Caco2

• etc.

Step 0

Wrap Up

“They care mostly about

Accessing our data through

API Knime Pipeline pilot”

“They want a product they can

use right out of the box”

New Reaxys Medicinal Chemistry is supporting Hit to lead and lead

optimization process by providing relevant and high quality data to scientists

by improving

Computational Chemists

High quality data on many

different topics (efficacy , ADMET,

Animal models)

Large Amount of data to Perform

models

Medicinal Chemists

Accessing the data through third

party tools

Reaxys Medicinal Chemistry is able to support both Computational and

Medicinal chemist

2

1

pX concept competitive advantage

• Augment (not replace) original data

• Make it possible to compare affinity of compounds using different reported metrics

Examples: IC50, Ki % inhibition

• Make it possible to search for active compounds regardless of metric reported

• Insure end users to encompass all the affinity data that they are searching for without

being an expert (knowing all the parameters and units used in publications)

• Facilitate analysis using third party tools (Spotfire, Pipeline Pilot) through the export.

pX it’s a unique way of quantifying affinity of compounds on targets

Parameter Filter

22

pX concept ?

Parameter Grinder

IC50, Ki, % Inhibition, %,EC50, pKi, ED50, pIC50, AUC, Emax(%),

Concentration, Cmax, nH, pA2, % Stimulation, Tmax, Fold

increase, t1/2 el, Rate, Number, Kd, pEC50, pKb, IA (%), Time, Km,

ID50, Delta, Vmax, Cl, Clint, Ue(%), pD2, %max, Kb, Bmax, Cavg,

Pressure, Amount, t1/2, Cl/F, Cmin, MED, fu, F(%), Dose, ClR,

AUC i/AUC, LD50, Frequency

PARAMETERS RELATED TO CONCENTRATION









pX is computed

Filter value for concentration

based parameters

Normalization to a single

comparable metric

Original values are preserved; this is an additional computed descriptor

23

Computation of pX value: - log (Affinity) and affinity results


pX = pIC50 etc….

Remark

If values are expressed in Weight/voluem( like g/l), they are first converted in M (using molecular

weight, animal/tissue weight or volume)

Results are expressed as –log10 (affinity)


pX= -log10(IC50)


How the pX is Calculated ? : ICF (25 ≤ F<95)

Like pICF, pECF, pEDF, pIDF, pLCF, pLDF are transformed into pIC50, pEC50, pED50,

pID50, pLC50, pLD50 using

Results are expressed as –log(affinity)

ICF, ECF, EDF, IDF, LCF, LDF where 25≤ F <95 are transformed into IC50, EC50, ED50,

ID50, LC50, LD50


pX= 𝐩𝐈𝐂𝟓𝟎 = 𝐩𝐈𝐂𝐅 − 𝐥𝐨𝐠𝟏𝟎𝟎−𝐅

𝐅where 25≤ F <95

IC50= 𝑰𝑪𝑭𝟏𝟎𝟎−𝑭

𝑭where 25≤ F <95 and pX=-log(IC50)

How the pX is calculated? : -log (Affinity) results with Modulators


If pIC50 etc….≤ 5 pX = 1

If pIC50 etc….> 5 pX= pIC50 etc … (Without modulator for pX)

Results are expressed as –log10 (affinity) with modulator s <,#<,<=,<<

Results are expressed as –log10 (affinity) with modulator s >,#>,>=,>>


pX= pIC50 etc … (Without modulator for pX)

How the pX is calculated? : Affinity results with Modulators


If IC50 etc….> 10 µM pX = 1

If IC50 etc….≤ 10µM pX= -log(IC50) etc … (Without modulator for pX)

Results are expressed as affinity with Modulators >,#>,>=,>>


pX= -log(IC50) etc … (Without modulator for pX)

Results are expressed as affinity with Modulators <,#<,<=,<<

How the pX is calculated? : affinity and –log(affinity) results and Ranges


If pRangemax –pRangemin < 3 pX = pRangemax

If pRangemax –pRangemin ≥ 3 pX is not calculated

Results are expressed as –log(affinity) with Ranges


If Rangemax

Rangemin< 1000 pX = -log(Rangemin)

If Rangemax

Rangemin≥ 1000 pX is not calculated

Results are expressed as affinity with ranges

How the pX is Calculated ? : % inhibition

Results are expressed % of inhibition

% of inhibition are converted into IC50 when

a concentration of the tested compound is

available using the following equation and

assumptions

- Hill slope = 1 (nh)

- % of inhbition between 25% and 95%

- Concentration of the compound is not Available

pX is not calculated

- Concentration of the compound is available as :

Range pX is not calculated

Single value pX is calculated as follow

o If %inhibition <25 pX = 1

o If 25 ≤ % inhibition <95 pX =-Log (IC50) using eq.1

o If % inhibition ≥ 95 % inhibition =95 and pX =-Log (IC50) using eq.1

% inhibition is available as Single value

- Concentration of the compound is not Available


- Concentration of the compound is available as :

Range pX is not calculated

Single value pX is calculated as follow

%inhibitionaverage=(%inhibitionmax+%inhibitionmin)/2

o If %inhibition Average <25 pX = 1

o If 25 ≤ % inhibition Average <95 pX =-Log (IC50) using eq.1

o If % inhibition Average ≥ 95 % inhibition Average =95 and pX =-Log

(IC50) using eq.1

% inhibition is available as Range

Eq.1

How the pX is Calculated ? : Qualitative results

- Not Active (NA)

pX = 1

- @ Active

Concentration of the compound is not Available


Concentration of the compound is available

Range pX = -Log [Concentration min]

Single value pX = -Log [Concentration]

Results are expressed as Qualitative

webinar: new rmc - your lead_optimization solution june082017

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