ELEM Biotech

Are you getting the maximum information out of your assay data when you make go/no-go calls? #Regulatory frameworks like ICH E14/S7B Q&As recognize that appropriately qualified in silico models may contribute to an integrated cardiac safety assessment and highlight the importance of incorporating uncertainty in experimental inputs. Here's how V.HEART Discovery gets you there: 💊 Discard unsafe candidates #early: Use QT-prolongation predictions to anticipate proarrhythmic risk at the preclinical stage and decide which compounds are worth taking forward. 💊 Explore the #whole IC₅₀/Hill space: Characterise compounds from single values or full probability distributions, following the CiPA paradigm, and toggle saline buffer versus plasma to capture environmental shifts. 💊 Go from 0 to 50× Cmax and #beyond: See the QT response across the full concentration range and protein-binding conditions, including high-exposure and abuse scenarios. 💊 Obtain #sexspecific proarrhythmic potential: Assess QT and arrhythmia risk across every channel-measurement combination on sex-specific models, capturing a known driver of TdP risk. Test it on your own compound: Try V.HEART Discovery: feed it IC50/Hill values or full probability distributions, toggle saline versus plasma, and see the sex-specific QT response across the range. 👉 https://lnkd.in/eWdexsJb #VHEARTDiscovery #AssayVariability #Preclinicalsafaty

GALACTIC-HF showed that omecamtiv mecarbil works best in the most severely affected patients. But not why. In January, ICH M15 made one principle concrete: a model earns trust when its reasoning can be #traced from result back to cause, not simply when its output lines up with an outcome. Here's what that looks like in practice: 💊 #GALACTIC-HF gave us the outcome: a modest benefit from omecamtiv mecarbil in HFrEF, concentrated in patients with the lowest ejection fraction, with no clear change in symptoms. What it couldn't give us was the why, which hearts respond, and through what mechanism. 💊 That "why" is what simulation makes visible. Digital Twins can isolate individual factors (ion-channel behaviour, myofilament kinetics) and run the same virtual patient across different treatments, reaching phenotypes rarely enrolled in a real trial. So we simulated omecamtiv mecarbil across a range of virtual HFrEF phenotypes.  A few insights our #DigitalTwins made visible: 1 - The same treatment produced different outcomes depending on ventricular remodelling and geometry, as prolonging systole shortens diastole: the benefit persists when filling is preserved and diminishes when it is not.  2 - We were able to assess safety margins that the clinical trial was unable to examine, such as impaired diastolic relaxation at higher concentrations. The model doesn't replace the trial. It interprets it, turning a single result into mechanistic hypotheses about responder subgroups that can guide the design of the next trial. Curious how this could be applied to your study? Contact us: https://lnkd.in/eSm5nzbS #MechanisticHearts #HeartDisease #HeartFailure #HFrEF #CardiacAmyloidosis #ObstructiveHCM #VirtualHumanHearts

Last week we highlighted a blind spot in early development: assay variability is measured, then averaged away... and with it, part of the confidence behind every go/no-go decision. ELEM platform V.HEART Discovery was developed to give you the maximum information out of your data: 💊 Keep the full experimental spread instead of collapsing replicates into a single IC₅₀, and propagate it at scale.  💊 Explore the entire exposure range, including supratherapeutic and overdose levels that can't be tested ethically any other way, on sex-specific models.  💊 Translate it into the clinical values your decisions actually depend on.  #Regulatory frameworks like ICH E14/S7B Q&As recognize that appropriately qualified in silico models may contribute to an integrated cardiac safety assessment and highlight the importance of incorporating uncertainty in experimental inputs. Test it on your own compound: Try V.HEART Discovery: feed it IC50/Hill values or full probability distributions, toggle saline versus plasma, and see the sex-specific QT response across the range. 👉 https://lnkd.in/eWdexsJb  #VHEARTDiscovery #AssayVariability #Preclinicalsafaty

Two patients with the same cardiac pathology take the same drug. The outcome: two completely different responses.  Why? 🎥 Our scientist Eva Casoni breaks down one of the hardest questions in heart failure: why patients with the same diagnosis respond so differently to the same therapy?    The answer isn’t in the diagnosis but in the heart itself: degree of remodelling, ventricular geometry, the state of the myocardium: these differ from patient to patient, and they change how a drug actually acts on the muscle.     Two hearts that look alike on paper can be mechanistically very different.  Ahead of #ESC2026 in Munich, we sat down with Eva to understand why virtual HFrEF hearts responded differently to omecamtiv mecarbil, watch the full video and learn more! Make the ‘unobservable’ visible: reveal the underlying biophysical mechanisms in your experiments to make safer, more informed decisions. Contact us: https://lnkd.in/ebi4Djc6 #MechanisticHearts #HeartDisease #HeartFailure #HFrEF #CardiacAmyloidosis #ObstructiveHCM #VirtualHumanHearts

Your ion channel assay already provides you with more information than just an IC₅₀ value. The same compound gives a different IC50 from one lab to the next, one replicate to the next, in saline buffer versus plasma. ⚠️ Still, we summarise those into a single average and move on. But measure the same channel on a different cell and the number moves: in one loperamide study, the same channel returned 45, 69 and 115 nM across replicates, and shifted almost 10-fold again between saline buffer and plasma. 💊 Everyone knows this variability exists. What's missing is a way to get the maximum information out of this data, this early, and translate it into clinical values of interest. Do you want to see the full picture of your compound, best and worst case included, before taking go/no-go decisions? Let's have a chat.   👉 https://lnkd.in/eUyFuvjk    Here’swhat it looks like to integrate your data into our platform V.HEART Discovery:  👉 https://lnkd.in/eigxcynV #VHEARTDiscovery #AssayVariability #Preclinicalsafaty

The quiet risk in every trial isn’t that the drug doesn’t work. It’s that it works for a subgroup you didn’t enroll.    A diluted treatment effect. A mismatched population. An endpoint that looked right on paper and didn’t hold up in Phase II. By the time you find out, you’ve often lost 2–3 years.    Part of the problem is who gets studied. Conventional testing runs on relatively homogeneous cohorts, and the patients who’ll actually take the drug, with heart failure, cardiomyopathy, ischaemia or prior infarction, carry altered physiology that changes how they respond.     In a recent paper authored by ELEM Biotech, we built 512 virtual subjects across healthy hearts and diseased hearts, covering five pathologies: 💊heart failure 💊dilated cardiomyopathy 💊hypertrophic cardiomyopathy 💊ischaemia 💊myocardial infarction ...with sex-specific models, represeting patients conventional studies leave out.    That’s what V.HEART-Trials platform puts in your hands.   More precise patient selection. Higher statistical power on population that matters. Fewer expensive surprises in Phase II/III.    👉 See how ELEM's V.HEART Trials supports Clinical Development teams: https://lnkd.in/evXqEvbM   #ClinicalDevelopment #PatientStratification #DigitalTwins #HeartFailure #DrugDevelopment More info on the paper? 👉https://lnkd.in/eQubPcsj #VHEARTTrials #VirtualCohorts #preclinicalsafety #preclinicalefficacy

What happens if you take #Loperamide beyond the recommended dose? And is there a difference in the effects between males and females?    Our recent paper offers some mechanistic answers to these clinical #uncertainties. Here are two examples:    💊 Female virtual hearts reached arrhythmic risk at ~427 ng/mL of plasma Loperamide, compared to ~849 ng/mL in male virtual hearts. That's roughly half the concentration. 💊 At therapeutic doses (Cmax ≈ 4 ng/mL), there's no risk at all. But once concentration climbs to just ~130–145 ng/mL, around 30-fold higher, mean QT prolongation already exceeds 10 ms, well before the arrhythmic threshold is crossed.    This isn't just a data point.    It's a mechanistic signal, aligning with growing clinical evidence, that women are more susceptible to drug-induced QT prolongation.    This is a reminder that "is this drug safe?" is the wrong question.    The right question is: safe for whom, and under what conditions?    Curious what this kind of analysis could reveal about your own compound?  Check ELEM V.Heart platforms  👉 https://lnkd.in/ea_4Ys4e

What does a pharma company need to see before integrating a Digital Twin platform into a #regulated trial?    It's not just about the science.    Before a SaaS platform can be used in a regulated clinical environment, it needs to demonstrate something harder to build than an algorithm: a documented, auditable quality system.    ISO 13485 and IEC 62304 are the two standards that define what "reliable healthcare software" means in practice, they cover from risk management and traceability to software development lifecycle documentation.    At ELEM Biotech, we worked with Alira Health to implement a QMS fully aligned with both standards, designed specifically for a SaaS and agile development environment.    The result: a compliance framework that does not slow down innovation but makes it auditable.    For pharma and clinical research organizations evaluating digital tools for regulated trials, this kind of framework is no longer a differentiator, but a baseline expectation.    📄 Read the full case study https://lnkd.in/exGAD8Vt    👉 Discover how the ELEM Digital Twins platforms support Pharma and Clinical Development teams:  https://lnkd.in/eujRTJkR   

How is AI impacting digital twins' viability in drug development?  Cardiac #DigitalTwins provide a mechanistic representation of cardiac physiology, enabling the study of drug responses across diverse patient phenotypes, risk profiles, and electrophysiological conditions.  📄 In our latest publication, we present an #AI-enhanced approach capable of reproducing the predictions of cardiac simulations while reducing execution by 5 orders of magnitude.  Trained on 900 3D cardiac simulations and developed separately for male and female physiology, these models enable rapid assessment of cardiac risk under uncertainty in #DrugEffects.  The result? 💊 Exploration of #CardiacSafety scenarios in real time 💊 Large-scale #VirtualPopulation studies 💊 Efficient quantification of #variability and uncertainty 💊 Faster and more informed preclinical #decision-making  📄 Read the paper: https://lnkd.in/e4nAg7cC 👉 Check our platform V.HEART Trials: https://lnkd.in/eujRTJkR