The use of Informatics Approaches in Cheminformatics
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The use of Informatics Approaches in Cheminformatics. Alexander Tropsha Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy. Overview of Current Projects Background on Cheminformatics

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Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

The use of Informatics Approaches in Cheminformatics

Alexander Tropsha

Laboratory for Molecular Modeling,

UNC Eshelman School of Pharmacy


Outline

Overview of Current Projects

Background on Cheminformatics

Examples of Application Projects: Data Retrieval  Modeling Testable Hypothesis Generation  Validation

OUTLINE


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

C-C=C-O

> Database of compounds (with their measured activities for multiple targets)

> Tools to visualize and navigate into chemical space.


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

D

E

S

C

R

I

P

T

O

R

S

Physico-Chemical

properties (logS, BP,

MP, logK etc.)

Biological

activities

Structure-Activity Relationships (SAR) modeling


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Computational Chemical Biology

C-ChemBench / CECCR project

Complementary Ligands Based on Receptor Information (CoLiBRI)


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Computational Chemical Biology

Protein Structure-Function

relationships modeling

Simplicial Neighborhood

Analysis of Protein Packing

(SNAPP)


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Activity/Function prediction for molecules

Empirical Rules/Filters

Similarity Search

Consensus QSAR models

VIRTUAL

SCREENING

~102 – 103

molecules

~106 – 109

molecules


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Activity/Function prediction for molecules

Protein-ligand recognition


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Cheminformatics and Structural

Bioinformatics

Selected Models

Descriptors and QSAR approaches

(modeling techniques, applicability domain definitions etc.)


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Cheminformatics and Structural Bioinformatics

Tools for chemical

data mining

Tetrahymena Pyriformis

Computational Chemical Toxicology


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

The Laboratory for Molecular Modeling

Principal InvestigatorAlexander Tropsha

Research ProfessorsClark Jeffries, Alexander Golbraikh, Hao Zhu, Simon Wang, M. Karthikeyan

Graduate Research AssistantsChristopher Grulke, Nancy Baker, Kun Wang, Hao Tang, Jui-Hua Hsieh, Rima Hajjo, Tanarat Kietsakorn, Tong Ying Wu, Liying Zhang, Melody Luo, Guiyu Zhao, Andrew Fant

Postdoctoral Fellows

Georgiy Abramochkin, Lin Ye, Denis Fourches

Visiting ResearchScientistsAchintya Saha, Aleks Sedykh, Berk Zafer

MAJOR FUNDINGNIH

- P20-HG003898 (RoadMap)

- R21GM076059 (RoadMap)

- R01-GM66940

- GM068665

EPA (STAR awards)

- RD832720

- RD833825

Research ProgrammerTheo Walker

System AdministratorMihir Shah

Adjunct MembersWeifan Zheng, Shubin Liu


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

toxicity prioritization & screening

environmental toxicity screening

What is Chemoinformatics?

Dr. Frank Brown introduced the term “chemoinformatics” in the Annual Reports of Medicinal Chemistry in 1998:

“The use of information technology and management has become a critical part of the drug discovery process. Chemoinformatics is the mixing of those information resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug lead identification and organization”

In fact, Chemoinformatics is a generic term that encompasses the design, creation, organization, management, retrieval, analysis, dissemination, visualization and use of chemical information.

http://www.bioinfoinstitute.com/chemoinfo.htm

Slide courtesy of Ann Richard


Nih s molecular libraries initiative in numbers

NIH’s Molecular Libraries Initiative in numbers

NIH Roadmap Initiative

Molecular Libraries Initiative

ECCR (6)

Exploratory

Centers

Predictive

ADMET

(10)

PubChem

(NLM)

4 Chemical Synthesis

Centers

MLSCN (9+1)

9 centers

1 NIH intramural

20 x 10 = 200 assays

CombiChem

Parallel synthesis

DOS

4 centers + DPI

100K – 1M compounds

  • Current SAR matrix

  • (as of May 25, 2007):

  • - 256 different MLSCN bioassays

  • over 140,000 chemicals

  • 29,558 compounds categorized as “active” in at least one MLSCN bioassay

Expected

1M compounds

200 assays


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

increasing complexity

increasing uncertainty

SAR

structure-activity

relationships

increasing relevance to RA

Chemocentric view of biological data

Toxicity Risk Assessment


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

2D Substructure Searching

Quantitative Structure-Activity relationships (QSAR)

2D Similarity Searching

Pharmacophore mapping

3D Substructural Searching

Docking

Molecular modeling

Decision trees

Molecular Diversity Analysis

Neural Networks

Quantum mechanical

Virtual Screening

Graph theory

Cluster analysis

Data Mining

Semiempirical

Molecular mechanics

Multiple linear regression

Principal components analysis

Inductive logic reasoning

ADMET

Genetic algorithms

Scoring functions

Property filtering

Active Analog

Drug-likeness

Free-Wilson

Hansch

  • Pharmaceutical Sciences

  • Drug Discovery

  • Chemical Design

  • Materials Science

  • Green Chemistry

  • Agricultural

  • Pesticides

  • Food Science

  • Polymers

  • Atmospheric chemistry

  • Environmental Studies

  • Green Chemistry

  • Predictive Toxicology


Key point focus on externally validated predictions

Largefraction are confirmed actives

Key point: Focus on Externally Validated Predictions

External database/library

SAR dataset

Input

Cheminformatics Magic

Small numberof computational hits

Output

Real Test


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Cheminformatics Analysis of Assertions

Describing Drug-Induced Liver Injury

in Different Species

In Collaboration with BioWisdom, UK


Background

Drug Induced Liver Injury (DILI) is one of the major causes of drug toxicity, both during clinical development and post-approval

Animal studies, and clinical trials on limited populations, are used to establish drug safety; both appear insufficient

A wealth of published information that could deepen our understanding the mechanisms of DILI is available, but the information is scattered in distributed published works, using inconsistent language

Background


Introduction to the safety intelligence program sip

An industry-sponsored initiative that embraces the expertise of it’s pharmaceutical members and other stakeholders to build the world's most comprehensive intelligence resource for use in improving drug safety assessments.

The Safety Intelligence System

The largest forever-expanding collection of known effects of chemicals occurring in the different tissue, drugs effects on clinical biomarkers of tissue injury and drug molecular mechanisms.

Facts (assertions) derived from:

Biomedical literature

Regulatory documents: EMEA EPARs, FDA NDAs

Label Data And many more…

Introduction to the Safety Intelligence Program (SIP)

5,700 pathologies 8,500 compounds 192,000 facts 1 interface


Intelligence network build process

Structured Data Sources

e.g, GO, UMLS, SWISS_PROT

Unstructured Data Sources

e.g, Medline, Patents, FDA SBAs

Noun Phrase Discovery

Raw Assertion Discovery

Relationship Discovery

User Defined

Term List

Relations Typing

Semantic Normalisation

Chemistry Canonicalisation

Public Domain Sources

Licensed Sources

Proprietary Sources

Intelligence Network Build Process

Meta-Search

Sofia Terminology &

Ontology

Data Source Descriptors

Structured

Data Loader

Spiders

Concept Maps

Selected Corpus

Automated

Assertion Generation

Pass

QA

QA

Fail

Pass

DocView

(manual validation)

Intelligence Network

Pass

Slide courtesy of Julie Barnes, Biowisdom


Species concordance study design

The Safety Intelligence System contains comprehensive assertional meta-data describing >5,800 effects of >8500 compounds in the liver

E.g. ‘Acetaminophen INDUCES Hepatocyte Death (mouse)’ (pathological effect)

E.g. ‘Prednisolone SUPPRESSES Collagen Synthesis (human)’ (physiological effect)

A subset of the above assertional meta-data, referenced by MEDLINE or the EMEA EPARs, were exported from the Safety Intelligence System for analysis

The data were restricted to therapeutic products only

The compounds were assigned to human, rodent or non-rodent groups according to the species in which the effect was reported

The concordance of drug-induced liver effects across humans, rodents or non-rodents was determined

Species Concordance Study Design


Species concordance of drug induced liver effects assertions evidenced by medline

14,600 assertions, 1061 compounds

Large data set – lending itself to quantitative analyses

Non-rodent data are less well represented than human and rodent

Species Concordance of Drug-Induced Liver Effects: Assertions Evidenced by MEDLINE


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Objectives

Can we employ cheminformatics approaches to validate

assertions of drug-induced liver effects in different species?

Can we identify chemotypes that define species-specific

liver effects?

Can we establish chemistry driven rules for concordance (or lack

thereof) between chemical effects on humans vs. non-humans?


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

BioWisdom

Safety Intelligence System

Primary data sources

Assertional meta-data generated using SofiaTM platform

Assertion refinement

Assertion export

Chemical curation, fragment analysis & QSAR

SIP Members

Project Workflow


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Used assertions evidenced by MEDLINE, rather than EMEA EPARs, because of their greater quantity

Used rodent and human data to build the model (knowing that non-rodent data are sparse in MEDLINE)

Used non-rodent data (where a liver effect was observed) to validate the model

Study Design


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Curation of Chemical Data

Step 1 : all inorganic molecules have been removed, as well as

those having no available SMILE strings. (993 of 1061 molecules remaining)

Examples:

Zinc chlorideCl[Zn]ClFerrous sulfate[Fe+2].[O-]S(=O)(=O)[O-]

Sulfur[S]Cobalt dichloride[Cl-].[Cl-].[Co+2]

Manganese chloride[Cl-].[Cl-].[Mn+2]Activated charcoalC

cis-Diaminedichloroplatinum[NH4+].[NH4+].[Cl-].[Cl-].[Pt+2]

Step 2 : 2D structures were obtained from the SMILE strings, using JChem software

from ChemAxon. Then, all counter-ions have been removed and molecules have

been neutralized, using ChemAxon Standardizer. (+aromatization,

+normalization of nitro groups) (989 compounds remaining)

Example:

Na+

Step 3 : manual molecular cleaning to correct some structures and to remove

compounds with non-sensible SMILES or duplicates

(951 of 1061 molecules remaining)


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Data transformation for the revised Venn diagram

Species profile for each compound (951) was retrieved from the original data automatically with a program written in Delphi.

only

only

only

For the cheminformatics analysis, we assumed that

each compound has been tested in all species, i.e., humans, rodents and non-rodents.

“1” = known liver effect

“0” = no liver effect


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

The Venn Diagram of the Curated Dataset

HUMAN

(650)

RODENT

(685)

292

236

257

110

12

26

18

Total number

of compounds:

951

NON-RODENT(166)


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

1. Clustering of compounds in the chemistry space*

C*C*C-C=O

Calculation of fragment

descriptors

C*C-C=O

C-C=O

C-C

C=O

C*C

Sequences of Atoms/Bonds

Inputs for

clustering

algorithm

*ISIDA is developed in the group of Prof. A Varnek, Univ. of Strasbourg.


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

1. Clustering of 951 compounds in the chemistry space

For cluster analysis we used fragment descriptors, hierarchical algorithm, Euclidean similarity between compounds, and a complete linkage between clusters.

Small clusters identified with high levels of similarity between compounds.


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

1. Clustering of compounds in chemical space

Example 1: Barbiturate derivatives; sedation/anaesthesia

a

b

c

d

ID = 45

HUMAN = 0

RODENT = 1

NON-RODENT = 0

ID = 76

HUMAN = 0

RODENT = 1

NON-RODENT = 0

ID = 93

HUMAN = 0

RODENT = 1

NON-RODENT = 0

ID = 543

HUMAN = 0

RODENT = 1

NON-RODENT = 0

Example 2: a = cladribine, b = clofarabine, c = cordycepin; all anticancer drugs

a

b

c

ID = 201

HUMAN = 1

RODENT = 0

NON-RODENT = 0

ID = 208

HUMAN = 1

RODENT = 0

NON-RODENT = 0

ID = 223

HUMAN = 0 (???)

RODENT = 1 (???)

NON-RODENT = 0


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

1. Example 1: Assessing potential data gaps

d

b

c

a

Allobarbital

HUMAN = 0

RODENT = 1

NON-RODENT = 0

Aprobarbital

HUMAN = 0

RODENT = 1

NON-RODENT = 0

Barbital

HUMAN = 0

RODENT = 1

NON-RODENT = 0

Methohexital

HUMAN = 0

RODENT = 1

NON-RODENT = 0

  • Recent mining of MEDLINE did not identify any evidence for these compounds having human liver effects

  • Basic searches in google (e.g. barbital, human, hepatotoxicity) did not reveal evidence for these compounds having human liver effects

  • The apparent lack of human liver effects may be due to these compounds being used for sedation/anaesthesia where lower doses and shorter exposures may be used than in animal studies


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Recent mining of MEDLINE did not identify any new evidence for 2a and b having rodent liver effects

Recent mining of MEDLINE did identify an effect of c in a human hepatocellular cell line

1. Example 2: Assessing potential data gaps

a

c

b

Cladribine

HUMAN = 1

RODENT = 0

NON-RODENT = 0

Clofarabine

HUMAN = 1

RODENT = 0

NON-RODENT = 0

Cordycepin

HUMAN = 0 (???)

RODENT = 1 (???)

NON-RODENT = 0

  • However, EMEA EPAR data in the Safety Intelligence System did identify b as having rodent liver effects (no rodent liver effects identified for a)


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

1. Clustering of compounds in chemical space

Example 3: a. amiodarone (antiarrhythmic agent), b. benzarone (used for treatment of

peripheral vascular disorders), c. benzbromarone (uricosuric agent, used for gout),

d. benziodarone (vasodilator).

b

a

ID = 98

HUMAN = 1

RODENT = 1

NON-RODENT = 0

ID = 60

HUMAN = 1

RODENT = 1

NON-RODENT = 1

c

d

Does this compound lack human liver effects ?

ID = 100

HUMAN =0

RODENT = 1

NON-RODENT = 0

ID = 99

HUMAN = 1

RODENT = 1

NON-RODENT = 0


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Recent mining of MEDLINE did not identify any new evidence for 3d having human liver effects

1. Example 3: Assessing potential data gaps

Does this compound lack human liver effects ?

Benziodarone

HUMAN = 0

RODENT = 1

NON-RODENT = 0

d

  • However, a basic search in google (e.g. benziodarone, human, hepatotoxicity) did reveal that the drug caused hepatotoxicity in humans (inferred)


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

1. Clustering of compounds in chemical space

Example 4: Estrogen-like compounds

Estradiol

ID = 329

HUMAN = 1

RODENT = 1

NON-RODENT = 1

b

2-methoxyestradiol

ID = 8

HUMAN = 1

RODENT = 1

NON-RODENT = 0

a

Estrone

ID = 333

HUMAN = 1

RODENT = 1

NON-RODENT = 0

d

Estriol

ID = 332

HUMAN = 0

RODENT = 1

NON-RODENT = 0

c

Ethinyl estradiol

e

ID = 338

HUMAN = 1

RODENT = 1

NON-RODENT = 1


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Recent mining of MEDLINE and a basic search in google (e.g. estriol, human, hepatotoxicity) did not identify any new evidence for estriol (c) having human liver effects

1. Example 4: Assessing potential data gaps

c

Estriol

HUMAN = 0

RODENT = 1

NON-RODENT = 0


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

1. Clustering of compounds in chemical space

Some clusters have been identified in which compounds share highly

molecular structures and also, toxicity profiles for H, R and NR.

This information is highly important to identify chemotypes that define species-specific DILI effects.

However, in some clusters, similar compounds appear to display different toxicity profiles.

These cases may correspond to missing or unreported data, and highlight areas for gap-spotting or additional experimental investigation.


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

2. Analysis of chemical fragment distribution

RODENT

ONLY

HUMAN

ONLY

A

B

Compounds found to show liver effects

for humans only

Compounds lacking liver effects

for humans

Are there some differences in fragment distributions between compounds displaying human vs. rodent specific effects?


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

STRUCTURE REPRESENTATION

Viewed by

computers

Viewed by chemists

Viewed by another

molecule

naphtalen-1-amine


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Graphs are widely used to represent

and differentiate chemical structures,

whereatoms are verticesand bonds

are expressed as edges connecting these vertices.

MOL File

Vertices

Molecular graphs allow the computation of numerous indices to compare them quantitatively.

Edges

Molecular descriptors


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

2. Analysis of fragment distributions within sets A and B

Fragment type

Fragment type

FA

FB

ΔF

FA

FB

ΔF

C-N-C71.649.022.6

C-C-C-N-C50.028.022.0

C-C-C-N58.937.421.5

C-C-N-C64.043.620.4

C-C-N-C-C39.820.619.2

C-N86.467.718.7

C-C-N76.359.117.1

C-N-C-C-N24.27.816.4

C-C-C-N-C-C30.915.215.8

C-N-C-C-N-C21.25.815.3

N-C-C-N24.69.714.8

C*N35.220.614.5

C*C80.166.113.9

C-C-N-C-C-O22.08.613.5

C-C-N-C=O29.216.013.3

C*C*N33.119.813.2

C-C-N-C-C-N18.66.212.4

S-C23.310.912.4

C-C-N-C-C-N-C17.85.812.0

C-S-C15.33.511.8

C-N-C-C-O29.217.511.7

C-N-C=O37.726.111.6

C*C*C*C70.859.111.6

C-S-C-C13.61.911.6

C-C-N-C-C=O17.45.811.5

O-C-C-N-C=O15.74.311.4

C=C-N15.33.911.4

C-N-C-C=O19.98.611.4

C-N-C=C14.02.711.3

C*C*C75.063.811.2

C-C-C86.975.911.0

N-C-C-N-C-C-O12.71.910.8

C-C-C=O47.937.410.5

O=C-C-N-C=O15.75.410.2

C-C-C-N-C-C-N14.84.710.2

S-C-C14.44.310.1

N-C=O42.832.710.1

C*C*C*N23.313.210.1

C*N*C29.719.89.8

C-C-C-C-N33.123.39.7

C-C-C-N-C-C=O13.13.59.6

N-C*N15.76.29.5

C-C=C-N12.73.59.2

N-C-C-N-C-C=O11.42.39.1

C=C-C-O14.45.49.0

C-C-C-N-C-C-C14.45.49.0

C-C=C-N-C11.42.78.7

S-C-C-C11.42.78.7

N-C-C=O20.812.18.7

C-C-C-C-N-C27.118.78.4

C-C*N17.48.98.4

Etc.

FA = Fragment Frequency (%) for (Human Only – 236 compounds)

FB = Fragment Frequency (%) for (Rodent Only – 257 compounds)


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

2. Differential fragment frequency distribution

FA = Fragment Frequency in A

FB = Fragment Frequency in B

ΔF = ( FA - FB)


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

3. Binary QSAR based classification

Class A

(248)

Class B

(283)

RODENT

ONLY

HUMAN

ONLY

Compounds NOT affecting liver in humans

Compounds known to affect liver in humans only

Can we predict the compound class from its structure only ?


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Principle of QSAR/QSPR modeling

D

E

S

C

R

I

P

T

O

R

S

Quantitative

Structure

Property

Relationships

Introduction

C

O

M

P

O

U

N

D

S

0.613

0.380

-0.222

0.708

1.146

0.491

0.301

0.141

0.956

0.256

0.799

1.195

1.005

P

R

O

P

E

R

T

Y


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Principle of QSAR/QSPR modeling

C

O

M

P

O

U

N

D

S

P

R

O

P

E

R

T

Y

D

E

S

C

R

I

P

T

O

R

S

Quantitative

Structure

Property

Relationships

Introduction

0.613

0.380

-0.222

0.708

1.146

0.491

0.301

0.141

0.956

0.256

0.799

1.195

1.005


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

3. QSAR based classification

Using SUPPORT VECTOR MACHINES (SVM)

Accuracy (%) = (number of compounds correctly predicted )/(total number of compounds)

Modeling set

5 fold CV

Modeling set

Accuracy

External set

Accuracy

Fold

Descriptors

Model ID

1 62.3% 88.2% 71.0% 217

62.9% 77.6% 67.3% 162

fragments

Dragon

2 64.9% 81.2% 64.2% 112

67.5% 81.2% 55.7% 197

fragments

Dragon

3 62.4% 91.3% 64.2% 194

65.2% 91.1% 61.3% 198

fragments

Dragon

4 64.9% 99.3% 72.6% 208

62.1% 84.9% 68.9% 151

fragments

Dragon

5 63.3% 82.6% 68.9% 205

61.9% 94.4% 70.8% 175

fragments

Dragon

NB: Preliminary results; could be improved.


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

3. QSAR based classification

Class A

(248)

Class B

(283)

RODENT

ONLY

HUMAN

ONLY

18

EXTERNAL SET

(18 compounds reporting

no liver effects

in humans or rodents)

QSAR MODELS


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

3. QSAR based classification

Modeling set

5 fold CV

Modeling set

Accuracy

External set

Accuracy

Descriptors

Model ID

Compounds

18 62.9% 92.5% 77.8% 206

64.0% 97.9% 66.7% 141

Fragments

Dragon

14 of 18 compounds are predicted

to lack liver effects for humans.

4 compounds are predicted to have human

liver effects. BUT:

Missing/unreported data ???

IN THE MODELING SET:

Sulfadimethoxine (ID=819)

Human = 1

Rodent = 0

Sulfadoxine (ID=820)

Human = 0

Rodent = 0


Alexander tropsha laboratory for molecular modeling unc eshelman school of pharmacy

Recent mining of MEDLINE did identify evidence for pyrimethamine/sulfadoxine (fansidar) causing hepatitis in patients

3. Sulfadoxine: Assessing potential data gap

Sulfadoxine

Human = 0

Rodent = 0

Missing/unreported data?

  • Normally, combinations would be excluded from these analyses


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