Synthetic route design remains a critical bottleneck in small‑molecule drug discovery, especially as teams juggle complex targets, aggressive timelines, and sustainability expectations. Computer‑assisted retrosynthesis has already transformed how chemists explore chemical space; however, the creative, collaborative aspects of route design often still happen offline spread across slides, notebooks, and screenshots.

In this talk, Dr. Ewa Gajewska will introduce the new SYNTHIA® Whiteboard, an interactive workspace built directly into SYNTHIA® Retrosynthesis Software. The Whiteboard allows chemists to pull in pathways, reactions, and molecules from any SYNTHIA® analysis and assemble them on a flexible canvas, then edit, combine, or redesign routes using an integrated molecule editor. This creates a continuous workflow from automated pathway generation to human‑driven refinement and decision‑making, all within a single environment. Through practical drug discovery–oriented examples, attendees will see how to merge routes from multiple analyses, design convergent and divergent strategies, and incorporate green chemistry or portfolio constraints—such as building-block availability or regulated lists—directly into their designs.

The session will also highlight collaboration features that enable teams to share Whiteboards, comment on routes, and prepare presentation‑ready pathway diagrams for project reviews and translational discussions. 

By the end of the webinar, participants will understand how the SYNTHIA® Whiteboard elevates retrosynthesis software from a powerful route‑design engine into a shared digital space where chemists can brainstorm, prioritize, and confidently select executable synthetic pathways for drug discovery and development. 

Learning Objectives:

1. Describe how modern computer‑assisted retrosynthesis tools can support small‑molecule drug discovery from hit to candidate selection.

2. Identify at least two ways digital route‑design workflows can reduce synthesis risk and timelines by revealing alternative disconnections, starting‑material options, or greener pathways.

3. Explain how interactive, collaborative route‑design environments can improve communication and decision‑making between medicinal, process, and analytical chemists.

Primary mitochondrial diseases caused by mutations in the nuclear and mitochondrial genome  represent the largest group of inborn errors of metabolism with a prevalence of about 1 in 4,300. During the last five years [1] the number of clinical trials for “Mitochondrial Diseases” has almost quadrupled. The very first drug approved by the FDA in February 2023 was Skyclarys (RTA-408) for the treatment of Friedreich’s ataxia. In September 2025 the FDA approved Forzinity™ (Elamipretide, SS-31) for treatment of Barth Syndrome. The European Medicines Agency (EMA) approved in 2015 Raxone (Idebenone) as treatment of visual impairment in patients suffering from Leber’s Hereditary Optic Neuropathy (LHON) and in 2023 Lumevoq (GS010) as gene therapy for the same condition. Russia  approved already in 2012 mitochondrial targeted Visomitin (SKQ1) for the therapy of dry eye syndrome. Focusing on new experimental drug entities (New Chemical Entities, NCEs)  I will give an update of the state-of-the-art in the field of mitochondrial drug development. I will analyze to what extent growing knowledge over the past two decades about the etiology and pathogenesis of mitochondrial diseases is reflected in the design and development of new experimental drugs. Considering all currently registered clinical trials involving NCEs, I will evaluate how far away we are from a cure of mitochondrial diseases. 

[1] V. Weissig. Drug Development for the Therapy of Mtochondrial Diseases. Trends in Molecular Medicine 2020, 26, 40-57

Learning Objectives:

1. discuss the urgent need for the development of drugs for mitochondrial diseases

2. summarize the multitude of molecular drug targets for mitochondrial diseases 

3. analyze major challenges for the treatment of mitochondrial diseases

This presentation will position personalized medicine at a turning point where genomic tools must be translated from discovery into everyday clinical care. I will discuss how I believe this transition requires new models of clinical research, ethics, health economics, and information systems that can handle multigene data, real-world variability, and patient-centered implementation.

I will discuss why I believe side effects and variability in drug response should be treated as core scientific signals, driving the discovery of genomic markers and “double-preventive” strategies that prevent both disease and drug-induced complications. I recommend focusing on multigenic, array-based diagnostics and on integrating genetics with lifestyle, environmental factors, and primary care workflows to make personalized medicine clinically actionable.

I will discuss how I believe ethical frameworks must stratify informed consent according to clinical risk, protect privacy while enabling pharmacogenetic testing, and move beyond traditional race categories toward genetically informed population definitions. I recommend developing new intellectual property, reimbursement, and outcomes research models so that personalized medicine becomes a sustainable, reimbursed standard of care rather than a niche or experimental approach.

Learning Objectives:

1. Identify History of the concept: personalized medicine vs. healthcare

2. Discuss the Technology and Clinical Decision Support

3. Recognize business models in personalized medicine.

Among incurable inherited metabolic diseases, mitochondrial diseases represent a major therapeutic challenge, as they can be caused by mutations in oxidative phosphorylation genes that are encoded by either the mitochondrial DNA (mtDNA) or the nuclear DNA (nDNA). This fact hampers the generation of effective model systems, given the challenges of mtDNA engineering. One of the most severe forms of mitochondrial disease is Leigh syndrome (LS), a devastating neurological disease with no cure leading to early death in children. In this talk, I will present our ongoing efforts in advancing the understanding and therapies for LS using induced pluripotent stem cells (iPSCs) and brain organoids. I will show examples of innovative interventions for LS identified through deep learning screening or neuronal high-content screening. A repurposable drug that we discovered has received the designation of Orphan Drug Designation (ODD) from the European Medicines Agency (EMA) for the treatment of LS, and for this a clinical trial is now under development. Through collaborations with clinical scientists, we hope to translate our findings in the lab into concrete therapies for incurable pediatric mitochondrial diseases with highly unmet medical needs.

Learning Objectives:

1. Recognize the challenges of mitochondrial disorders

2. Discuss the potential of pluripotent stem cells and brain organoids

3. Outline potential repurposable drugs for the mitochondrial disease Leigh syndrome

Patient-derived xenograft (PDX) and organoid (PDO) models are powerful platforms for studying tumor biology and therapeutic response, but their scientific impact depends on the ability to systematically integrate and analyze the diverse data generated around them. As living biobanks grow in scale and complexity, researchers face increasing challenges related to data heterogeneity, reproducibility, discoverability, and cross-study comparability.

In this presentation, I will describe an integrated data ecosystem developed within the Princess Margaret Living Biobank to support translational research using PDX and PDO models. I will first introduce the types of clinical, molecular, and pharmacological data associated with patient-derived models and discuss key challenges encountered when scaling these resources. I will then outline a federated data infrastructure that harmonizes clinical metadata, multi-omics datasets, and drug screening results, supported by standardized analysis pipelines, shared code repositories, and user-facing query tools.

Finally, I will present a representative case study demonstrating how standardized data curation and integrated infrastructure enable efficient cohort assembly, robust analysis, and the identification of molecular determinants of drug response. This work illustrates how coupling biological insight with scalable data systems can accelerate biomarker discovery and improve the translational value of living biobank resources in precision oncology.
 

Learning Objectives:

1. Describe the major data types associated with PDX and PDO models and the challenges of managing and analyzing these data at scale.

2. Explain how integrated data curation and harmonization enable reproducible and comparable cancer research

3. Apply data-driven analyses using patient-derived models to inform therapeutic sensitivity and translational decision-making.

Non-animal methodologies are experiencing renewed enthusiasm driven by advances in AI, machine learning, and increasingly sophisticated cell-based systems. While these technologies represent genuine opportunities to improve translational relevance and reduce animal use, their current capabilities are often overstated. This presentation examines where non-animal models are truly creating value today, where they are falling short, and what must change for broader adoption to occur.

A major focus of the talk is the growing gap between expectations and reality for AI in drug discovery. Unlike large language models that were trained on vast, relatively clean datasets derived from the internet, biological data is scarce, noisy, heterogeneous, and deeply context-dependent. The underlying rules of biology are only partially understood, and the available datasets are orders of magnitude smaller than what would be required to fully train predictive systems. As a result, AI remains fundamentally constrained by data availability and biological uncertainty, limiting its ability to replace experimental systems rather than augment them.

The presentation also addresses advanced cell culture models, including organoids, microphysiological systems, and other complex in vitro platforms. While regulatory frameworks are often blamed for slow adoption, the primary barrier has been inconsistent performance and limited validation relative to established in vivo models. Similar to other technology transitions, regulation reflects technical maturity more than it dictates it.

The session concludes with a realistic outlook on how non-animal models can evolve, emphasizing validation, hybrid approaches, and incremental integration rather than wholesale replacement of existing paradigms.

Learning Objectives:

1. Analyze the fundamental data and biological constraints that limit the current impact of AI-driven approaches in drug discovery.

2. Evaluate why advanced cell culture and microphysiological systems have not yet achieved widespread adoption as replacements for animal models.

3. Assess realistic pathways for integrating non-animal models into drug development workflows based on validation, performance, and translational relevance.

A key challenge in combating cancers is tumor progression. As cancers grow and encounter selective pressures, they continually evolve, leading to metastasis, drug resistance, and worsening prognoses. Central to these changes are two related processes: angiogenesis and epithelial to mesenchymal transition (EMT). Angiogenesis, the growth of new blood vessels to fed the growing tumor, has emerged as a promising therapeutic target, and multiple drugs acting against the vascular endothelial growth factor (VEGF) pathway have entered clinical use. EMT is a developmental process reactivated in cancers, marked by changes in cell adhesion that permit metastatic spread and associated with altered therapy response. However, targeting EMT has proved challenging, and work is ongoing to identify novel treatment approaches.

Recently, in our search for drugs that would target cells that have undergone EMT, we identified the VEGF receptor inhibitor cediranib as having differential efficacy in mesenchymal-like vs. epithelial-like cancer cells. We have found this to be true across multiple types of cancer and, given efficacy in vitro in the absence of vasculature, independent of its anti-angiogenic effects. Here we report anticancer efficacy of cediranib and identify ERK1/2 as mediators of cediranib response in ovarian cancer. Future work in our group will continue to unravel the mechanisms responsible for these effects. Overall, this study and others like it have the potential to expand the utility of VEGF-directed agents for cancer therapy.

Learning Objectives:

1. List potential novel mechanisms of action for cediranib

2. Explain the molecular changes that occur during cancer progression

3. Compare genetic and pharmacologic means of modulating target protein activity in cells