Day 1: Systems Neuroscience Enters the Room

The summit opened with workshops, one of which I co-organized with Mikhail Kalinichev (Neurosterix Therapeutics, Geneva) and Rouba Kozak (Foundation for the National Institutes of Health). Mikhail brings deep experience from his years at GSK, Lundbeck, and Ipsen, having helped drive clinical development in schizophrenia-related cognitive impairment. Rouba previously led programs developing many neuro/psych relevant compounds with a focus on precision medicine.

Our workshop centered on how systems neuroscience and computational models of circuit dysfunction—what I’ve called “algorithmic circuit psychiatry”—can refine target selection, inform biomarker development, and guide trial design. This perspective has been underrepresented in drug development but is increasingly seen as essential for psychiatric disorders, where symptom clusters often reflect underlying circuit-level failures. See my earlier post here

A central question emerged: Should biomarker development occur during phase 1-3 clinical trials, or wait until after drug approval? The tension reflects the translational challenge in psychiatric drug development. Without predictive biomarkers, clinical trials must balance stringent selection criteria against practical timelines, often leaving drug developers flying blind.

Left to right: Michael Halassa, Mikhail Kalinichev, Rouba Kozak

Following our session, Paulo Lizano from BIDMC led a compelling workshop on integrating patient-centered outcomes, such as quality of life measures, directly into clinical trial endpoints. As the field increasingly recognizes that pure symptom scales may not fully capture meaningful change, incorporating functional and experiential outcomes feels both scientifically valid and deeply patient-centered.

Day 2: The Rise of Muscarinic Agents

By the second day, one theme dominated: muscarinic receptor agonism has become the most exciting frontier in schizophrenia drug development.

The KarXT/Cobenfy Story

Steve Paul delivered a masterful keynote tracing the long and improbable road to Cobenfy (xanomeline-trospium), the most important advance in psychosis pharmacotherapy since clozapine. Originally developed as a derivative of arecoline (from betel nut) for Alzheimer’s, xanomeline’s repurposing for psychosis required remarkable translational persistence.

Key highlights from Paul’s talk:

• Exceptional CNS penetration: A 10:1 brain-to-plasma ratio, highly atypical for psychiatric agents.
• Side effect complexity: Pro- and anticholinergic effects drive the need for more selective muscarinic agents.
• Domain-specific effects: Cognitive gains, especially in working memory and executive function, often decouple from standard PANSS reductions.

Paul emphasized that head-to-head trials against atypical antipsychotics remain a critical next step.

Steve Paul being introduced by Murali Gopalakrishnan

Steve Paul gave an inspiring and forward-looking talk

From Algorithm to $14 Billion

Andrew Miller, one of Karuna’s original R&D leads, described how 7,410 compound combinations were screened through an algorithm-driven selection process, ultimately resulting in the xanomeline-trospium pairing. Starting with just $4,000 (and critical support from the Wellcome Trust), Karuna’s journey culminated in its $14 billion acquisition by Bristol Myers Squibb—a biotech success story that underscores how unconventional mechanisms can lead to transformative breakthroughs.

Andrew Miller gave a superb talk about the development of KarXT/Cobenfy

Emerging Themes: Biomarkers, Stratification, and Digital Tools

Throughout the summit, multiple sessions drilled into the next central challenge: how to stratify patients, measure meaningful improvement, and de-risk clinical development earlier with predictive biomarkers.

Biomarkers: The Missing Link—and what’s up with Emraclidine?

The panel featuring Nick Brandon (Neumora), Hadile Ounallah-Saad (Clexio), Larry Park (AbbVie), and Rob Goldman (MapLight Therapeutics) emphasized that lack of biomarkers has plagued psychiatric drug development for decades. AbbVie made clear they are not abandoning Emraclidine despite Phase II setbacks. They attribute the setback primarily to suboptimal trial design rather than flaws in the compound itself—a refreshingly sophisticated view that acknowledges how heterogeneity, endpoint selection, and sample stratification can easily derail psychiatric trials.

Right to Left: Nick Brandon (Neumora), Hadile Ounallah-Saad (Clexio), Larry Park (AbbVie), and Rob Goldman (MapLight Therapeutics)

I was particularly happy with the opportunity to discuss early clinical real-world experience with Cobenfy. Having the people who developed this compound in the same room was surreal!

Digital Interventions for Negative Symptoms

Click Therapeutics showcased their work on digital interventions targeting experiential negative symptoms. Their augmented reality glasses, designed to enhance social salience during simulated interactions, represent a highly innovative, non-pharmacological circuit intervention.

The wonderful click team

I had particularly rewarding conversations with Click’s CMO, Shaheen Lakhan, about converging digital therapeutics with systems neuroscience. We’re now collaborating on a forthcoming article about the future of inpatient psychiatry, combining digital augmentation with circuit-level mechanistic frameworks. Stay tuned!

Novel Biology: Retroviruses & Neuroplastogens

• Jonathan Javitt presented provocative data implicating endogenous retroviral elements (HERV-W-ENV) in schizophrenia pathophysiology—a reminder that viral mechanisms may still yield unexpected insights.
• Rajiv Agrawal (Deluxe Therapeutics) introduced DLX-2270, a neuroplastogen targeting synaptic vesicle protein 2A density. While neuroplasticity-based interventions (e.g., ketamine in depression) have gained traction, their application in psychosis remains early-stage.

Long Acting Injectables (LAIs) and Early Intervention

Hannah Brown delivered an excellent talk on the value of long-acting injectables (LAIs) in first-episode psychosis, referencing mirror-image study designs like PRELAPSE. Reconnecting with Hannah—whom I trained alongside in residency—was a highlight, and her combination of methodological rigor and clinical pragmatism was outstanding.

Hannah Brown did a masterful job at emphasizing the importance of LAIs

Was great to connect after 12 years!

The Takeaway: A Field at Inflection Point

The closing day featured Hadile Ounallah-Saad’s exceptional talk on developing an M1/M4 agonist (CLE-905) with different properties than Xanomeline and one that may not need a peripheral antagonist like trospium! If this ends up panning out in human trials, it will be game-changing and will give us a shot at a muscarinic LAI! Go Clexio!

Hadile Ounallah-Saad giving a talk about Clexio’s lead compound CLE-905

My friend Zhong Zhong, CMO for OVID therapeutics gave a superb talk on KCC2 activators as a potential class of antipsychotic medications. My lab is involved in the preclinical side of this endeavor so we’re all hopeful this will end up having some clinical utility!

Zhong talking about KCC2 modulators as disease modifying agents in schizophrenia

Throughout the summit, there was palpable energy. After decades of stagnation, the field appears to have finally cracked open. For too long, psychiatric drug development has been stalled by perceived biological intractability and late-stage trial failures where clinical efficacy predicted pre-clinically failed to materialize. But this summit showcased the tools now available to change that trajectory:

• Systems neuroscience informing target selection
• Circuit models guiding conceptual framing
• Sophisticated biomarkers enabling patient stratification
• Digital augmentation expanding therapeutic options
• Novel molecular targets beyond dopamine

The muscarinic wave, exemplified by xanomeline, validates that genuinely novel mechanisms can succeed in psychiatry, while also reinforcing that circuit-level understanding must drive future drug development.

For those of us working in systems and circuits, it seems obvious that this is the type of neuroscience that pharma needs. This is but one of several facets of the algorithm circuit framework we have been working on for the last several years.

I left Boston energized and cautiously optimistic that we are witnessing the dawn of psychiatry’s own precision medicine revolution. The science is finally catching up to the clinical complexity. Now we need the discipline, patience, and collaborative ambition to build on this momentum.

This piece reflects my experience co-organizing the Systems Neuroscience workshop at the 2025 Innovation in Psychosis Therapeutics Summit and observations from three days of presentations, panels, and discussions with leading researchers, clinicians, and industry executives.

The molecular era of neuroscience has been productive. It gave us the tools to understand neurotransmitter systems, develop targeted receptor modulators, and generate narratives that we can explain to patients and families struggling to understand the burden of mental illness. The dopamine hypothesis, GABAergic interventions, and serotonergic medications established the scientific credibility of early biological psychiatry.

But as became clear throughout the summit discussions and pre-meeting workshops, the extreme focus on molecular details as the ‘mechanism of action’ misses a larger point. In contrast to other organ systems where knowledge of molecular and cell biology gives one a pretty reasonable understanding of organ function (Cytochrome P450 functioning in a hepatocyte tells you quite a bit about what the liver does in drug detoxification and actin sliding on myosin in a cardiomyocyte explains a lot of what the heart does), understanding action potentials and synaptic transmission tells us very little about how thinking works.

Think of it this way: studying individual brain cells and their chemical signals to understand mental illness is like trying to understand a movie by analyzing the pixels on your TV screen. You can learn a lot about how pixels work (their color values, brightness, refresh rates) but that won’t tell you whether you’re watching a comedy or a thriller, or why the plot doesn’t make sense. The story emerges from how all those pixels work together in patterns over time.

This is psychiatry’s fundamental challenge. We’ve become experts at the “pixels” (the molecular mechanisms, neurotransmitter systems, and individual brain cells). But mental illness isn’t a problem with individual pixels. It’s a problem with how the brain’s software processes information, makes decisions, and builds our sense of reality.

Building the Brain’s “Flight Simulator”

What we really need is something like a flight simulator for the brain—computational models that can show us how molecular changes ripple through neural circuits to affect thinking, emotion, and behavior. Just as pilots use flight simulators to understand how adjusting one control affects the entire aircraft’s performance, we need brain simulators to predict how a new medication will affect a person’s ability to think clearly, regulate emotions, or maintain stable beliefs about reality.

Take depression, for example. Molecular framing focuses on “low serotonin” or other types of “chemical imbalances.” But computational models indicate that certain forms of depression have more to do with how the brain learns from rewards and punishments. Imagine your brain has a built-in prediction system that’s supposed to help you learn from experience, when good things happen, it should update your expectations upward; when bad things happen, it should adjust appropriately. In depression, this system over-learns from negative experiences and under-learns from positive ones, creating a downward spiral of increasingly pessimistic predictions about the future.

Of course, this algorithm has a neural implementation—involving specific circuits, cell types, and neuromodulators—but the unit of analysis most relevant to symptoms and their relief is the algorithm itself, not the transmitter systems.

Understanding this algorithmic dysfunction opens up entirely new treatment possibilities. Instead of just trying to boost serotonin levels, we can target the specific computational processes that have gone awry.

Recent clinical trials are demonstrating exactly this approach. Researchers have used computational models to predict which patients with depression will respond to cognitive behavioral therapy by measuring how their brains process reward prediction errors during learning tasks (Rzepa et al., 2017). Other studies have shown that computational measures of effort-based decision-making can predict which patients will relapse after stopping antidepressants, identifying a persistent algorithmic dysfunction that outlasts mood symptoms (Berwian et al., 2020).

This isn’t just about having more treatment options. It’s about matching the right intervention to the right computational problem. Some patients might benefit most from medications that restore balanced reward learning. Others might need brain stimulation that resets dysfunctional prediction circuits. Still others might respond best to digital therapies that provide targeted algorithm retraining.

The Missing Piece: Systems Neuroscience

Here’s what’s been missing from the molecular-to-computational translation: systems neuroscience. Over the past two decades, this field has exploded with revolutionary tools and insights that completely change how we understand brain function. We can now record from hundreds of neurons simultaneously, manipulate specific cell types with optogenetics, trace connectivity patterns across entire brains, and interpret brain dynamics with unprecedented precision.

These advances have revealed something remarkable: the brain operates through large-scale computational principles that emerge from how circuits are organized and interact. We’ve discovered that the cortex implements hierarchical predictive processing—constantly generating predictions about incoming information and updating these predictions when they’re wrong. We’ve learned that the dopaminergic system implements temporal difference learning in the brain. We’ve found that the hippocampus works like a sophisticated pattern-completion system, able to reconstruct entire memories from partial cues by leveraging the same mathematical principles that power modern AI memory networks.

I have been fortunate to establish my lab around the time many of the technical advances in systems neuroscience had come to the fore. Using these tools and working with many talented students and collaborators, we made a series of surprising observations that challenged a long held dogma: the thalamus, considered a major sensory relay station in the brain, plays critical roles in higher cognition. In my own lab, we’ve used these tools to understand how the thalamus regulates cortical state switching—an operation fundamental to cognitive flexibility and psychiatric dysfunction.

In fact, most of the thalamus in our brains as humans is unlikely to play much of a role in sensory processing. Instead, it dynamically regulates cortical dynamics and implements context-dependent gating of information flow. This discovery emerged from combining well-controlled animal behavior (building on years of work by pioneers in the field), optogenetic manipulations, and high-density neural recordings.

The prefrontal cortex is a critical area in psychiatry because its neurons form coalitions that provide mental simulations, working memory and action plans. My lab among others discovered that inputs from the thalamus are critical for maintaining and switching prefrontal representations underlying these algorithmic processes. In essence, when you need to switch between different mental tasks, thalamic circuits provide the actual switching signals, determining the timing and specificity of cortical state changes.

This has profound implications for understanding cognitive deficits in disorders like schizophrenia. There is good neuroimaging evidence to suggest thalamic dysfunction in schizophrenia and we are in early stages trying to determine whether that may be related to the inability of patients to maintain accurate models of the world, revise their mental simulations when they are implemented or some combination of such processes. Close integration between animal and human work is key to making good progress.

Most importantly, this systems-level understanding opens new therapeutic possibilities. Rather than targeting broad neurotransmitter systems, we might develop interventions that specifically modulate thalamocortical dynamics. For instance, understanding how cholinergic signaling regulates thalamic gating could inform more precise pharmacological approaches. Similarly, targeted neuromodulation techniques could potentially restore proper state regulation in these circuits. However, translating these insights into clinical interventions will require careful validation of the computational models we develop in animals and their relevance to human psychiatric conditions.

Algorithmic Circuit Psychiatry: The Bridge We Need

This is where systems neuroscience becomes the essential bridge between cellular neuroscience and computational science. We can now connect specific molecular mechanisms to circuit dynamics to algorithmic functions—creating what I call “algorithmic circuit psychiatry.”

The framework works like this: cellular neuroscience identifies the molecular players (receptors, channels, neurotransmitters), systems neuroscience reveals how these molecules shape circuit dynamics and information processing, and computational science provides the mathematical frameworks to understand what algorithms these circuits implement. Instead of having three separate fields talking past each other, we can trace a coherent path from molecules to circuits to algorithms to symptoms.

Designing the Next Generation of Trials

The clinical application of this framework involves a systematic approach: first, we decompose patient symptoms using computational methods, fitting their behavioral data into mathematical models and extracting specific algorithmic parameters. Next, we use precision neuroimaging to identify the neural circuit alterations underlying these computational dysfunctions. Finally, we leverage mechanistic models built from animal studies to predict which pharmacological and behavioral interventions will restore healthy circuit-algorithm function in each individual patient.

This approach could fundamentally transform psychiatric treatment by replacing trial-and-error prescribing with mechanistically-informed precision medicine. Mental illness is not caused by broken molecules, but by maladaptive computations implemented in circuit dynamics. The treatment of the future won’t correct a chemical imbalance—it will recalibrate an algorithm.

Instead of cycling through different medications hoping something works, we could predict treatment response based on each patient’s specific pattern of circuit-algorithm dysfunction. The computational parameters tell us what’s broken, the neuroimaging reveals where it’s broken, and the mechanistic models suggest how to fix it.

The Path Forward: Evolution, Not Revolution

What’s most exciting about this moment is that we’re not throwing out decades of neuroscience research. Instead, we’re building on that solid molecular foundation to create more sophisticated, comprehensive approaches to psychiatric treatment.

This evolution is already transforming drug development in several ways. For smarter target identification, instead of hunting for individual molecules to drug, we can identify key bottlenecks in dysfunctional brain algorithms and ask what molecular interventions might restore healthy computational processes.

We’re also developing better animal models. Instead of relying on crude behavioral measures that don’t really capture human mental illness, we can focus on algorithmic functions that are conserved across species and ask whether potential treatments restore these core computational abilities.

This approach enables more meaningful biomarkers. Instead of simple blood tests or brain scans, we can develop assessments that capture how well someone’s brain algorithms are functioning, providing much richer information for treatment selection and monitoring progress.

Finally, understanding how different interventions work across levels opens up possibilities for rational combination therapies. We might pair a medication that fixes a molecular problem with brain stimulation that resets dysfunctional circuits and cognitive training that helps retrain maladaptive algorithms.

An Invitation to the Future

The conversations following my Boston summit report suggest that the field is ready for this evolution. Researchers across academia and industry are recognizing that our most exciting recent advances have come from thinking about mental illness as a multi-level problem requiring multi-level solutions.

This isn’t about abandoning the rigorous molecular research that brought us this far. It’s about using that foundation to build something much more powerful—treatments that are informed by molecular mechanisms, guided by circuit-level insights, and targeted toward restoring the algorithms that generate healthy thinking and emotion.

We have the molecular foundation. Circuit-level insights are maturing rapidly. Computational frameworks are emerging from labs around the world. The clinical need remains as urgent as ever.

The pieces are finally in place for a new generation of psychiatric treatments—ones that don’t just manage symptoms, but recalibrate the brain’s computational machinery for healthy thinking, feeling, and action.

The time for integration is now.