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MARRYING IN VITRO AND COMPUTATIONAL APPROACHES TO IMPROVE DRUG SAFETY AND EFFICACY
Prof. dr. Paul JenningsDivision of Molecular and Computational Toxicology,
Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam,
Contact e‐mail : [email protected]
MARRYING IN VITRO AND COMPUTATIONAL APPROACHES TO IMPROVE DRUG SAFETY AND EFFICACY
Human in vitro methodologies are becoming invaluable tools for uncovering mechanisms of chemical‐induced perturbation. This coupled with techniques such as transcriptomics, metabolomics, proteomics and high content imaging can produce large amounts of data, capturing large chunks of biology. For such systems to be applicable to safety assessment, the kinetics of the chemical in the systems needs to be determined, and reverse dosimetry to the whole body should be applied. Computational approaches, including docking and dynamic models can also be used to supplement cheminformatics to predict chemical interaction with enzymes and receptors. The integration of biological data streams, with cheminformatics, kinetics and in vitro to in vivo extrapolation requires the marriage of in vitro and computational approaches. The improvement of efficacy and safety prediction is dependent on the evolution of these individual aspects in and integrated synergistic way.
Keywords: Stress response, transcriptomics, safety, efficacy
Jennings, P., 2015. “The future of in vitro toxicology.” Toxicol. Vitr. 29, 1217–1221. doi:10.1016/j.tiv.2014.08.011
Prof. dr. Paul JenningsDivision of Molecular and Computational Toxicology,
Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam,
Contact e‐mail : [email protected]
Is this chemical safe ?
42Cyclosporine A
NOEL under specific dosing regimes.
Is this chemical to other animals that are not rats ?
??? X 100 NOEL
Is this chemical safe ?
How is this chemical interacting with biological systems?
• Cheminformatics• Transporter interaction• Absorption, Metabolism• Biological Perturbations ‐ In vitro test systems
Mechanistic based toxicology
6Modified from Mel Anderson’s Toxicity Testing in the 21st Century: A Vision and a Strategy 2007
Classical approach
Cellular perturbation Cell Death
Exposure
Tissue Dose
Biological Interaction
Biological Inputs
Normal biological function
Homeostasis
Cell survives but altered phenotype
Allostasis
Mechanisms: Sublethal injury / stress
Integrated OMIC approaches and computational biology
Adaptive responses: Transcriptional Reprogramming
“We dance round in a ring and suppose,But the Secret sits in the middle and knows”
The secret sits; Robert Frost
7
Adaptive Stress Response Pathways / “Toxicity Pathways”
• p53 ‐ DNA damage stress response• Nrf2 – oxidative stress response• Unfolded protein response – 3 branches ER
stress response• HIF‐1 alpha – hypoxic stress response• MTF – heavy metal stress response • HSP – heat shock stress response
Stress Response pathways
Ligand and xenobobiotic activated pathways• Nuclear receptors (NR 1‐4) [48 in humans]• Aryl hydrocarbon receptor
Inflammatory stress response pathways• NF‐kB• STAT
Others• MAPKs, FOXOs, energy stress (AMPK, SREBF1, SIRT, CREB and the mitochondria)
An overview of transcriptional regulation in response to toxicological insult. Jennings et al. Archives of Toxicology 2013
Transcriptionally Regulated
Nrf2
CUL3(Ubiq E3) Keap1
Nrf2maf
ARE/EpRE
Nrf2
nucleus
cytosol
ROS
degraded
actin
Keap1
uuuuu
electrophile
1. Glutathione metabolism and recyclingGCLC, GCLM, GSR
2. Xenobiotic metabolism and transportGST, ABCC
3. Reduction of oxygen species and quinonesHO‐1, NQO1, SOD‐1, TXNRD1, UGT1A1, SRXN1, AKR1B10, AKR1C1
↑ transcription
NAM
10
Animal models
► Poor prediction of adverse drug reactions (high rate of false negatives (30 to 50 %), X % of false positives).
► Valid ethical concerns (3Rs).
► High cost and time.
► Not well suited to molecular investigations at a cellular level.
Human based in vitro systems
? Depends on the model. Human skin models out perform animals for corrosion and irritation.
Ethically sound.
Cheaper and quicker (in theory).
Ideally suited to molecular investigations –allowing much deeper mechanistic information.
Why do we test drugs on/in animals ?
What options do we have for human cells?
Cell lines*Primary Stem cells
• Same donor• Phenotype does not
respect donor or tissue origin
• Every time a new donor• Difficult to acquire tissue
• ES – same donor, but never lived
• iPSC – can choose a living donor
12
Limonciel et al Archives of Toxicology, 2018.
RPTEC/TERT1: nephrogenic transcription factor PAX8, the tight junction protein claudin 2 (CLDN2), SLC7A5 and SLC3A2 (the genes encoding the proteins for the large neutral amino acid transporter LAT1), the brush border enzyme gamma glutamyl transferase (GGT1), collagen 4A (COL4A), methionine adenosyltransferase 2A (MAT2A), secreted phosphoprotein 1 (SPP1), mal, T‐cell differentiation protein 2 (MAL2), ATP binding cassette subfamily C members (ABCC 4 and 5), phosphofructokinase (PFKP), gamma‐butyrobetaine hydroxylase 1 (BBOX1), vimentin (VIM), ATPase Na+/K+ transporting subunit beta 1 (ATP1B1), the proton pump (ATP6VOE1), glyceraldehyde 3‐phosphate dehydrogenase (GAPDH), ribosomal protein S7 (RPS7), amyloid beta precursor protein (APP), myosin X (MYO10), NAD(P)H quinone dehydrogenase 1 (NQO1), mucin 1 (MUC1), adhesion molecule with Ig like domain 2 (AMIGO2) and E74‐like factor 3 (ELF3).
HepaRG: Plasma proteins albumin (ALB), haptoglobin (HP), transthyretin (TTR), apolipoproteins (APOA1, APOC1, APOE1), fibrinogen (FGG, FGP) and complement proteins (C3, C1R and CFH), genes involved in xenobiotic metabolism, including carboxylesterase 1 (CES1), cytochrome P450s (CYP3A4, CYP3A5, CYP2E1), N‐acetyltransferase 2 (NAT2) and other liver‐specific genes, including fatty acid binding protein 1 (FABP1), orosomucoid 1 and 2 (ORM1, ORM2), ABCC2, alcohol dehydrogenase (ADH1A and 1B), SLCO1B1, SLCO2B1 and bile acid‐CoA:amino acid N‐acyltransferase (BAAT).
Also highly express several genes associated with cancer, many belonging to the GAGE family (GAGE ‐1, ‐2, ‐3, ‐4 and ‐12).
13
Cyclosporine A
14
• Widely used immunosuppressant• Highly lipophilic: Therapeutic plasma levels in high nanomolar to low micromolar
• Metabolised via CYP3A4/5• Extrusion apical MDR1• Biological binding partner is cyclophilin (A, B, C and D)
Experimental Design
TCX – Illumina HT12 v4 BeadChip arraysPTX‐ iTRAQMTX – NMR and MSFXN – morphology, TEER and lactate
Kinetics –CsA LC‐MS
Veh. control, low and high conc [non cytotoxic]
1 ml
2 ml
Wilmes et al. 2013 Journal of proteomics, PMID: 23238060
Biokinetic data and modelling
Supernatant Cellular
15 µM
5 µM
15 µM
5 µM
Non‐linear cellular accumulation.
TCX all probes
Wilmes et al. 2013 Journal of proteomics, PMID: 23238060
TF log ratio* z-score p-value No.
15 µ
M D
ay 1
NFE2L2 (Nrf2) 5.72 9.2E-10 110XBP1 0.82 4.549 5.2E-02 41ATF4 1.04 4.205 1.8E-06 37
KDM5B (JARID1B) 0.05 3.938 2.4E-05 40
CDKN2A (p16) 3.677 3.8E-03 49
15 µ
M D
ay 3
NFE2L2 (Nrf2) 5.092 1.2E-09 123XBP1 1.30 5.023 7.5E-02 46ATF4 1.16 4.303 3.2E-05 38
KDM5B (JARID1B) -0.37 3.545 6.2E-03 37
CDKN2A (p16) 2.894 4.5E-02 50
15 µ
M D
ay 1
4 NFE2L2 (Nrf2) 5.784 3.4E-11 70CDKN2A (p16) 4.064 7.0E-03 27
ATF4 0.89 3.729 2.7E-10 30XBP1 0.68 3.703 1.4E-04 31
KDM5B (JARID1B) -0.88 2.979 1.3E-03 21
Tran
scrip
tion factors
Proteomics: Cyclophilin B secretion
18Wilmes et al. 2013 Journal of proteomics, PMID: 23238060
Metabolomics
19
PCA of all featuresHeat map of 192 sig altered features
Cellular (targeted) Supernatant (untargeted)
sMTX predicts internal CsA conc
Supernatant CsA induced metabolomic alterations (192 entities) correlated well with tissue concentrations.
Wilmes et al. 2013 Journal of proteomics, PMID: 23238060
Integrated omics: Cyclosporine A @ 15 µM
In vitro: Efficacy vs Chemical induced Stress
Therapeutic window
efficacy
Cyp‐B
N=1N=2N=3
• Cellular accumulation over threshold conc (overcomes P‐glycoprotein)
• Saturates internal lipid structures causing mitochondrial and ER disturbances leading to oxidative stress
• Therapeutic concentrations do not lead to cellular stress
• Including other organs including hepatic metabolism CsA concs will not reach 10 µM in vivo
24
Zhang et al.
25
The model proposed can be used to analyze and predict cellular response to oxidative stress, provided sufficient data to set its parameters to cell‐specific values. Omics data can be used to that effect in a Bayesian statistical framework which retains prior information about the likely parameter values.
An adverse outcome pathway (AOP) is structured representation of biological events leading to adverse effects and is considered relevant to risk assessment.[1][2][3] The AOP links in a linear way existing knowledge along one or more series of causally connected key events (KE) between two points — a molecular initiating event (MIE) and an adverse outcome (AO) that occur at a level of biological organization relevant to risk assessment.[2] The linkage between the events is described by key event relationships (KER) that describe the causal relationships between the key events.
EU‐ToxRisk – An Integrated European ‘Flagship’ Programme Driving Mechanism‐based Toxicity Testing and Risk Assessment for the 21st century – is a European collaborative project funded by the EU Framework Programme for Research and Innovation, Horizon 2020. With a budget of over 30 million €, the project started on 1st January 2016 and will last for a duration of 6 years.
Repeat dose toxicity and Development and Reproductive Toxicity
Bob van de Water
https://openrisknet.org/
an open e‐Infrastructure providing resources and services to a variety of communities requiring risk assessment, including chemicals, cosmetic ingredients, therapeutic agents and nanomaterials. OpenRiskNet is working with a network of partners, organized within an Associated Partners Programme.
29
“in three”
Funded by the Marie Skłodowska-Curie Action - Innovative Training Network under grant no. 721975.
Paul JenningsVrije Universiteit Amsterdam
Integrated in vitro and in silico tools
iPSC
differentiation
SendaiOct3/4, Sox2, Klf4, c‐Myc
Integrated Approaches to Testing and Assessment
Lung
Kidney
Liver
Brain
Blood brain barrier
Vasculature biokinetics
modelling
bioinformatics
Read Across and QSARAOP
AOP
AOP
AOP
AOP
AOP
* Reporters
Toxicity testing
o P ‐ glycoproteino MATE‐1o BSEP
Gene editing
transcriptomics
Cyclosporine A
How is this chemical interacting with biological systems?