Identifying CNS drugs requires unique considerations beyond efficacy

BIOAVAILABILITY – drug available in the body to act at target Inability to reach target in sufficient amounts during appropriate time window

LIMITS opportunity for efficacy – BBB, metabolism, efflux Caveat: Bioavailability DOES NOT guarantee drug efficacy STARTING POINT: How does an oral drug get into the CNS?

Quantification

LogBB = comparison of brain, plasma concentrations

Relative bioavailability %F = [AUCpo] / [AUCiv]

Absorption

Metabolism

Tissue Distribution

Time

[Drug]

Molecular properties influence how drugs are absorbed, how they are distributed, how they interact with transporters and metabolizing enzymes

Case study: Antihistamine CNS bioavailability changes impact adverse events

First-generation antihistamines characterized by sedative side effects Undesirable feature!!

Second-generation antihistamines lack drowsiness properties Better safety index

ON

HO

N

OH

OH

O

DIPHENHYDRAMINE FEXOFENADINE

Brain penetrant

Avoids penetrating

CNS

Avoids P-glycoprotein efflux

P-glycoprotein substrate

Antihistamines lacking sedative properties tend to possess limited CNS bioavailabilitycompared to antihistamines with drowsiness

Obradovic T et al. (2007) Pharm Res, 24, 318-327.

Case study: CYP2D6 metabolism alters bioavailability, impacts safety/efficacy

CYP2D6 - major isoform involved in CNS drug metabolism! Genetic polymorphisms affect CYP2D6 expression, function

ON

ON

OH

TAMOXIFEN 4-HYDROXY TAMOXIFEN

CYP2D6

“Ultra-rapid” metabolizer phenotype

“Poor” metabolizer phenotype

Increased CYP2D6 function

Decreased CYP2D6 function

CYP2D6 phenotype correlates with disease progression in breast cancer

Morphine toxicity risk with UM phenotype;Poor efficacy with PM phenotype

O

N

O H

H

HO

HO

N

O H

H

HO

CODEINE MORPHINE

CYP2D6

“Ultra-rapid” metabolizer phenotype

“Poor” metabolizer phenotype

Increased CYP2D6 function

Decreased CYP2D6 function

Bioavailability…it’s a big deal!

So, what can you do to find compounds that are bioavailable?

Hint: you don’t need to do in vivo testing just yet..

Molecular Properties 101: Physical properties influence how drugs interact with the body

Solubility, lipophilicity, size impact ADME outcomes

Absorption: Will the drug penetrate across the GI tract to the circulatory system?

Distribution: Will the drug remain soluble in the blood? Will it remain bound to plasma proteins?

Metabolism: Will the drug be chemically modified by CYPs? How much will be available to get to the target?

Excretion: How will the body eliminate the drug?

SOLUBILITYSOLUBILITY

ChargeCharge

IonizationIonization

DissolutionDissolution

LIPOPHILICITYLIPOPHILICITY

SIZESIZE

H-BondingH-Bonding

ShapeShape

AmphiphilicityAmphiphilicity

Charge DistributionCharge Distribution

LogP

MW

PSA

*Modifying one property has consequences on others

Figure modified from van de Waterbeemd H. (2009) Chem Biodiv, 6, 1760-1766.

Improving the odds: Using properties guidelines can increase bioavailability odds

“Rule of 5” - Christopher Lipinski Poor absorption/permeation

MORE LIKELY if: >5 Hydrogen bond donor atoms

(HBD) MW > 500 LogP > 5 N + O > 10

1990s: analyses used to identify ways to improve attrition due to poor bioavailability

Today = Smarter screening platforms

N

NHN N

HN

O

N

N

CAVEAT: The Ro5 is NOT CNS specific!

Gleevec (imatinib)

LogP 2.89 MW 493.6 PSA 86.28

HBD = 8

N + O = 8

NS

O

HNOOH

HNO

HN

ON

N

S

Norvir (ritonavir)

LogP 2.33 MW 720.6 PSA 202.26

HBD = 11

N + O = 11

CNS drug discovery properties analysis

What molecular properties are most relevant to CNS? LogP – lipophilicity,

solubility in octanol/H2O

MW – size PSA – polar surface area

(N’s, O’s)

How do I calculate these? Experimental

pION* www.pion.com CEREP www.cerep.fr Protocols – “home grown”

In silico – calculate estimated values derived from real structures ACD/Labs* Schroedinger ChemAxon*

*Discounts available for academics

DISCOVERY TIP:Prior to purchasing or

screening libraries – look at the property landscape. How

much is CNS relevant?

CNS drug discovery properties analysis – what are “good” values?

CNS drugs occupy a more restricted molecular properties space

Properties guidelines also depend on development status (hit versus lead versus drug)

Fragments

LogP < 3

MW < 300

PSA < 90

Oral Drugs

LogP < 5

MW < 500

PSA < 140

Rees et al. (2004) Nat Rev Drug Discov, 3, 660-672. Lipinski CA et al. (2001) Adv Drug Deliv Rev, 46, 3-26.

CNS Drugs

LogP < 4

MW < 400

PSA < 80

Chico et al. (2009) Nature Rev Drug Discov, 8, 892-909.

Case Study: CNS properties analysis identifies guidelines

Properties were computed using ACD Labs (v.11). Data shown are mean±SEM. Student’s t-test used to compare mean values with CNS means. *, p<0.05; ***, p<0.001.

Chico et al. (2009) Nature Rev Drug Discov, 8, 892-909.

PSA discriminates CNS+ better than LogP

Pgp+ compounds possess higher LogP, MW than

Pgp- compounds

Case study: Properties guidelines help prioritize CNS drug discovery efforts

Simple properties filters helped prioritize the top 6% of candidates! <100 compounds were synthesized from start lead clinical candidate.

Wing et al. (2006) Curr Alz Res, 3, 205-214. Chico et al. (2009) Drug Metab Dispos, 37, 2204-11. Chico et al. (2009) Nature Rev Drug Discov, 8, 892-909.

5 amines + 18 alkyl/aromatic groups =

1700+ possibilities

PSA <80Å2

MW <400LogP < 4

(80%)(80%)(80%)

Case study: Overlapping properties analyses focuses discovery efforts

Most property analyses focus on one outcome or endpoint… …but CNS bioavailability involves multiple outcomes

(penetration, metabolism for example). CNS+/CYP2D6- = good! CNS+/CYP2D6+ = bad!

Future direction of the field – perform properties analysis on multiple outcomes and “overlap” results

Query: where are we most likely to find compounds that are both CNS+ AND CYP2D6-? Approach: Superimpose properties to find “hotspots” associated with

CNS+/CYP2D6- candidates

Chico et al. (2009) Drug Metab Dispos, 37, 2204-11. Chico et al. (2009) Nature Rev Drug Discov, 8, 892-909.

Find the “sweet spot” of CNS+/CYP2D6- using overlapping analyses

CNS+/CYP2D6+ Avoid this

region

CNS+/CYP2D6-Minimized risk

of CYP2D6 involvement, but still have

CNS+

CNS+/CYP2D6-Minimized risk

of CYP2D6 involvement, but still have

CNS+

CNS+PSA ≤ 80Å2

LogP ≤ 4MW ≤ 400

Database summary statistics:

Multidimensional properties analyses helps refine “CNS” space

Wager et al. (2010) ACS Chem Neurosci, 1, 420-434. Wager et al. (2010) ACS Chem Neurosci, 1, 435-449

Analyzing properties associated with multiple ADME features helps identify more restrictive guidelines, increases probability of finding CNS+ compounds.

Takeaways – how can I use properties guidelines in my discovery efforts?

Library screening/selection Properties can help you focus screening

on most “CNS”-relevant members. Some libraries are more CNS friendly than

others.

Hit-to-lead refinement It is easier to add than subtract later! Start low – expect to increase as you

proceed Applying guidelines allows chemists to budget

their selections

Guidelines are guidelines – NOT rules Don’t get tripped up by numbers. Rationale

trumps rules!!

Resources

Experimental pION www.pion.com CEREP www.cerep.fr

In silico ACD/Labs Schroedinger ChemAxon

Fragments

LogP < 3

MW < 300

PSA < 90

CNS

LogP < 4

MW < 400

PSA < 80

Oral Drugs

LogP < 5

MW < 500

PSA < 140

Thank you for your time

Synthetic Chemistry Essentials for Biologists

February 2012

Heather Behanna, PhD

Biotechnology Research Associate

hbehanna@jmpsecurities.com

(312) 768-1795

17

18http://www.sciencecartoonsplus.com/pages/contact.php

19

An overview of the drug discovery process

Nature Review Drug Discovery,8, 892 2009.

20

The Drug Discovery Chemist

Synthetic chemistry-How to make things

Medicinal chemistry-What makes a drug

Pattern recognition and recall

21

Pattern recognition and recall

O2N NO2

NO2

O

SO

OO

N N

O

S

O

O

O

NHHN

HN

OCl

OHO

H

O

HN

N N

N

O

O

N

N

NH

TNTSalinsporamide – clinical trials for

cancer

Point of covalent attachment to proteins

Azo-blue

N

N

NH

F

F

22

Chemical space versus drug-like space

Lipinski, C and Hopkins A, Nature, 2004, 432(16) 855.

Nature Biotechnology 24, 805 - 815 (2006)

23

Scaffolds for drug design

Core structures (scaffolds) tend to be heterocycles

Rings (that can be involved in stacking and hydrophobic interactions

Heteroatoms (non-carbon atoms) for potential hydrogen bonding interactions

Heterocylces can interact with proteins through both hydrogen bonds and hydrophobic factors

Scaffolds must have synthetic “handles”

Accessible chemistry

NNF3C

SO

ONH2

N

N

NH

N

O

NN

N

N NH

N

N N

HN

NH2

24

Properties of scaffolds

Some scaffold changes or substitutions will drastically affect activity

Privileged scaffolds

Viagra Levitra

N

N

HNN

O

N

N

HNN

O

No serotonergic and dopaminergic activity

Strong M1 receptor ligand

N

NS

OO

O

N

HN

O

NN

N

NS

OO

O

NN

HN

O

N

The scaffolds of some drugs can be modified without changing the mechanism of action

Might show changes of ADME properties

25

An overview of the drug discovery process

Looking for a starting point – either binding or weak activity that can then be optimized

Obtainment of a Hit

26

How to get a hit?

High throughput screening

Screen a library for activity against a target or phenotype

Traditional assays

Adaption of patented compounds or natural products

Test for some activity and against others

Fragment screening

Screen for binding to a target (may not have activity)

Biophysical methods

27

High throughput screening (HTS)

Advantages:

Ability to screen hundreds of thousands of compounds in weeks

Automated systems

Novel in-house libraries

Disadvantages

Limited to chemical space in the library

Lead to discovery of “red flag” compounds

Generally larger than “optimal” leads

28

HTS pitfalls - Bad Hits and Frequent Hitters

J Chem Inf Model. 2007 Jul-Aug;47(4):1319-27.

NN

HO

Br

O O

O

OH

HO

O

N

Pattern recognition and recall

Compounds that are potent in HTS are not necessarily Hits!

29

Adaption of natural products

Genistein – natural product shown to have promise for:

Cancer (topoisomerase inhibitor)

Cystic Fibrosis (CFTR corrector)

Anthelmintic (inhibits glycolysis)

Tumor metastisis (MEK4)

Genistein Compound 46% cell viability

@ 50uM 36% 99%cell invasion, %

control 50% 45%

Genistein

Compound 46

F

O

O

O

O

O

OO

US 2010/0137425 A1

30

Fragment based approach

Fragments consist of

Low MW

Low LogP

High ligand efficiency (binding energy per atom)

Combination of hydrophobic and H-bonding properties

Fragments are screened for binding to a target

Expanded to gain efficacy

Structure assisted

Nature Reviews Drug Discovery 3, 660-672 (2004)

Curr Top Med Chem 7, 1600-1629 (2007); Current Topics in Medicinal Chemistry, 5, 751-762 (2005)

31

How can we do that?

32

Hit criteria

Regardless of how a hit is generated, it must pass certain criteria

Show potency in cell assays

Precursor to a drug, not just a ligand!

Show potential chemical handles for structure modification

Possess certain ADME properties

Quality of the library will strongly influence the chance of finding drug-like suitable hits

Fragment libraries tend to have better properties as hits than HTS libraries

Library properties should be considered

Interdisciplinary teams are best for hit evaluation

Not all active compounds are worth pursuing as a drug

Certain compounds come with “red flags”

33

An overview of the drug discovery process

“Hit to Lead”

Nature Review Drug Discovery,8, 892 2009.