E therapeutics : Interim Report for the six months ended 31 July 2023

Computing the Future of

Medicine™

Interim results for six months ended 31 July 2023

26th October 2023

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© 2023 e-therapeutics. All rights reserved | Interim Results 2023	2

Company Overview

Driving innovation at the intersection of AI and precision medicine

Our mission:

Integrating computational power and biology to discover life-transforming medicines

Cash and cash equivalents £24.8m 2022: £21.8m

Revenue
£0.2m	2022: £0.3m
R&D spend
£5.3m	2022: £3.1m
Operating loss
£7.0m	2022: £4.6m
Loss after tax
£5.6m	2022: £3.8m
R&D tax credit receivable
£2.5m	2022: £2.2m

Multi-disciplinary team (exc NED) 34 FTE 2022: 38

Share Price (25/10/23)

10.7p

Shares outstanding (25/10/23)

583.8m

Market cap (25/10/23)

£62.5m

London Boston

Company HQ

Interim results for six months ended 31 July 2023

© 2023 e-therapeutics. All rights reserved | Interim Results 2023	3

Our Approach

Integrating computational power and biology to discover life-transforming medicines

Therapeutic

Pipeline

World-class hepatocyte data	Proprietary chemistry platform	In-house pipeline of
resource with sophisticated	for potent and durable	GalOmic™ RNAi therapies
network biology analytics for	hepatocyte-specific mRNA	across broad range of
target ID and ability to automate	knockdown of novel targets	indications, with lead assets in
early stages of preclinical	identified by HepNet™	cardiometabolic disease and
development		haemophilia

© 2023 e-therapeutics. All rights reserved | Interim Results 2023	4

Traditional Approaches to Drug Development are Too Slow and Too Expensive

• Typical small molecule preclinical development takes a minimum of 5-10years.
• Enabled by computation and use of the RNAi modality, we can go from from gene target selection to disease model experiments in 6 months, costing less than $500,000 and IND ready in 3 years.
• This means we can rapidly develop multiple life-transforming RNAi medicines for the people that need them.

	Preclinical Development Timeline
Target	Drug	Animal	IND-enabling	ETX's RNAi platform enables rapid and
ID	Design	Disease Models	cost-effective drug development
	6 months	6-12 months	18 months
	<$500K	$500K - 1m	$4 - 6m
Typical Small Molecule Preclinical Development Timeline
	Target ID & Validation	Hit ID	Hit-to-Lead	Lead Optimisation	IND-enabling
			Minimum 5-10 years
					© 2023 e-therapeutics. All rights reserved | Interim Results 2023	5

Therapeutic

Pipeline

HepNet™

Our world-classhepatocyte-specific computational biology platform

HepNet™ is our proprietary computational biology platform,

built on the world's most comprehensive hepatocyte-specific

knowledgebase. It enables:

• Identification of novel targets for a wide range of diseases through sophisticated network analytics that account for the true complexity of biology
• Increased speed of execution by automating drug discovery and design processes
• Mining of 100s of integrated data sources to distil new mechanistic knowledge of hepatocyte biology

ETX data and knowledge covers…

12,091 expressed genes

1039 secreted proteins
461	proteins secreted
to blood

700 biological processes

© 2023 e-therapeutics. All rights reserved | Interim Results 2023	7

HepNet™

Our world-classhepatocyte-specific computational biology platform

Unstructured Data	Structured Data	Computational
From Various Sources	(Knowledge Graph)	Analysis	Outputs
Network Analytics
Literature	Patents		Hidden Gene-Disease	Novel Gene Targets
Grants		Links (scored)	siRNA Construct
Proteins		Machine Learning
ncRNAs		Design
			siRNA Efficacy
Pathways	Drugs
		Expert Review	Prediction
	Omics
GWAS
		Competitive Intelligence	Biological Insights

Clinical Trials	Intellectual Property
Animal Models	Patents
Literature Review

HepNet™ increases automation and provides us with the ability to identify novel

targets and rapidly design siRNA constructs.

© 2023 e-therapeutics. All rights reserved | Interim Results 2023	8

siRNA Efficacy Prediction

Using machine learning to predict siRNA efficacy and bypass in vitro screening

• Highly accurate model trained on proprietary, high-quality training datasets
• Trained model demonstrates high prediction accuracy, performance is superior to widely used algorithms (BioPredSi, ThermoComposition21)
• Enables identification of lead siRNA sequences in silico, minimising number of sequences that require screening
• We are now exploring further enhancement of predictions using large language models trained on mRNA sequences

Pre-AI Approach

Post-AI Approach

Maximum predicted mRNA knockdown

Predicted vs measured siRNA efficacy

(Validation Dataset)

Spearman Rank

Correlation ρ=0.85

Maximum measured mRNA knockdown (in vitro)

	Number of siRNA screened	Up to 400	<10
	Time to lead identification	6 months	1 month
	(potential clinical candidate)
	Cost of screening	$500,000	$50,000

HepNet™'s siRNA efficacy prediction already reduces preclinical development timelines and costs, with potential to enable bypassing of in vitro screening

© 2023 e-therapeutics. All rights reserved | Interim Results 2023	9

Enhancing Computation with LLMs

Transforming HepNet™ into a dynamic knowledge resource

• We are fully embracing the latest advances in generative AI and LLMs through integration with HepNet™ and creation of specialist LLM agents
• LLM agents trained on specific data such as scientific papers, mRNA sequences, hepatocyte- specific data, patents etc. will support target ID, target-indication evaluation and drug design
• This will enhance our ability to understand, reason, and infer from vast amounts of data, increasing automation and speed of ETX processes

© 2023 e-therapeutics. All rights reserved | Interim Results 2023	10