Neumora Therapeutics: A Story Of A Class II Biotech

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Neumora Therapeutics' lead asset, Navacaprant, shows weak efficacy in phase 2 trial, casting doubt on its success in upcoming phase 3 trials.The company's AI-driven precision medicine claims have not ...

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}\n.meta {color:#5e5c6d;font-size:13px;margin:0 0 .5em; }\na{text-decoration:none; color:#2a4b87;}\n.meta .head { display: inline-block; overflow: hidden}\n.head .h-thumb { width: 30px; height: 30px; margin: 0; padding: 0; border-radius: 50%; float: left;}\n.head .h-content { margin: 0; padding: 0 0 0 9px; float: left;}\n.head .h-name {font-size: 13px; color: #eee; margin: 0;}\n.head .h-time {font-size: 11px; color: #7E829C; margin: 0;line-height: 11px;}\n.small {font-size: 12.5px; display: inline-block; transform: scale(0.9); -webkit-transform: scale(0.9); transform-origin: left; -webkit-transform-origin: left;}\n.smaller {font-size: 12.5px; display: inline-block; transform: scale(0.8); -webkit-transform: scale(0.8); transform-origin: left; -webkit-transform-origin: left;}\n.bt-text {font-size: 12px;margin: 1.5em 0 0 0}\n.bt-text p {margin: 0}\n</style>\n</head>\n<body>\n<div class=\"wrapper\">\n<header>\n<h2 class=\"title\">\nNeumora Therapeutics: A Story Of A Class II Biotech\n</h2>\n\n<h4 class=\"meta\">\n\n\n2024-10-16 14:56 GMT+8&nbsp;&nbsp;&nbsp;<a href=https://seekingalpha.com/article/4727011-neumora-therapeutics-nmra-stock-story-of-class-ii-biotech><strong>seekingalpha</strong></a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n<p>Neumora Therapeutics' lead asset, Navacaprant, shows weak efficacy in phase 2 trial, casting doubt on its success in upcoming phase 3 trials.The company's AI-driven precision medicine claims have not ...</p>\n\n<a href=\"https://seekingalpha.com/article/4727011-neumora-therapeutics-nmra-stock-story-of-class-ii-biotech\">Source Link</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","isBrief":false,"type":0,"news_type":1,"symbol":"NMRA","symbol_name":"Neumora Therapeutics, Inc.","start_time":0,"source_url":"https://seekingalpha.com/article/4727011-neumora-therapeutics-nmra-stock-story-of-class-ii-biotech","article_id":"2475931702","we_media_id":null,"thumbnails":["https://static.seekingalpha.com/cdn/s3/uploads/getty_images/1833412195/image_1833412195.jpg"],"rights":null,"url":"https://stock-news.laohu8.com/highlight/detail?id=2475931702","pubTimestamp":1729061763,"sourceInfo":{"source_id":"seekingalpha","name":"Seeking Alpha"},"weMediaInfo":null,"summary":"Neumora Therapeutics' lead asset, Navacaprant, shows weak efficacy in phase 2 trial, casting doubt on its success in upcoming phase 3 trials.The company's AI-driven precision medicine claims have not ","collect":0,"end_time":0,"defaultTopTitle":"seekingalpha.com","property":[],"viewcount":null,"language":"en","relate_stocks":{"NMRA":"Neumora Therapeutics, Inc.","BK4007":"制药"},"translate_title":"Neumora Therapeutics：II类生物技术的故事","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"NMRA":0.9},"content_text":"Neumora Therapeutics' lead asset, Navacaprant, shows weak efficacy in phase 2 trial, casting doubt on its success in upcoming phase 3 trials.The company's AI-driven precision medicine claims have not materialized in clinical trial designs, undermining their differentiation strategy.Valuation models suggest Neumora's stock is overvalued, with a fair valuation significantly lower than the current market price.   JulPo/E+ via Getty Images Financial markets, or more broadly, entire economies, are well-known to exhibit a certain level of cyclicality. This is largely explained by mainstream economists through concepts like interest rates or credit cycles, which end up affecting spending and can lead to increased or decreased economic activity. Like the broad concept of cyclic recessions and expansions, one could say that financial markets have another type of cyclic event, often referred to as a \"bubble\". The oldest economic bubble on record is likely the Tulip mania of the 17th century, during which the price of a contract for a single tulip bulb reached levels equivalent to ten times the annual income of a skilled craftsman. A more recent example is the Dot-com bubble, which occurred in the late 1990s and was largely driven by the widespread adoption of what we now take for granted—the internet. Many say that we are currently in another bubble of sorts—the \"AI\" bubble. The technology, largely popularized within the folk by OpenAI and the growing use of ChatGPT, has found its way into many industries, perhaps at times without a solid rationale. The biotechnology industry is one such industry. This by no means implies that \"AI\" is useless in biotech; rather, the branding of how useful it is often has little to do with its actual utility. Perhaps Alex Telford describes it best in his blog post on \"a new breed of biotech.\" Alex quotes an executive who said, \"If you automate 5 processes with AI, and do the remaining 995 the old way—you’re still pretty much a regular biotech.\" Introduction Neumora Therapeutics Inc. (later “the company” or “sponsor”), a small biotech company focusing on neurological and psychiatric diseases, was co-founded by its current executive chairman, Paul L. Berns, in 2019. Shortly after, in 2020, the company merged with several smaller businesses that already possessed clinical assets. Notably, BlackThorn Therapeutics Inc., one of the merger partners, held rights to Navacaprant, now the most advanced drug in Neumora’s pipeline. BlackThorn originally spun out of the Scripps Research Institute in 2013, a renowned research institution. After multiple setbacks in developing its lead asset—BTRX-246040, a NORP antagonist that had failed two clinical trials at Eli Lilly before being licensed to BlackThorn—the company ultimately merged with Neumora. Neumora brands itself as a company utilizing high-level computational and analytical tools to address the challenges of neuropsychiatric research, including identifying reliable biomarkers and homogeneous patient populations. This tech-driven spirit, emphasizing AI and data integration, is evident in Neumora's claims (not so much in actions) are consistent with BlackThorn's original vision of leveraging artificial intelligence to analyze psychiatric data and identify patient subgroups most likely to respond to treatment. As mentioned supra, the lead asset in Neumora’s pipeline is Navacaprant (BTRX-335140, NMRA-140), a small molecule Kappa opioid receptor (KOR) antagonist. The drug has entered phase 3 trials for major depressive disorder (MDD) and a phase 2 trial for bipolar II disorder (BPD), with the first topline data readout for MDD expected in the fourth quarter of 2024. The sponsor is also investigating a range of neuropsychiatric disorders in earlier stages of development, including agitation in Alzheimer's disease (AD), schizophrenia, amyotrophic lateral sclerosis (ALS), and Parkinson's disease (PD). We believe that an in-depth analysis of the data concerning Neumora’s most material product—Navacaprant—is crucial for assessing the stock's short- to mid-term prospects. We intend to demonstrate that the rationale and proof-of-concept data are vague, and we have significant doubts about the transferability of these findings into the upcoming phase 3 readouts. Currently trading at ~$16 per share, corresponding to a market capitalization of approximately $2.5B, we believe that if Neumora’s KOR antagonist fails to deliver the anticipated results, the company's market capitalization will significantly decline. Thesis We believe that the clinical developments of Navacaprant for MDD are unlikely to replicate or improve upon the results presented in the proof-of-concept studies, which could result in a significant decrease in the stock price. Our skepticism is based on three main factors. First, drugs targeting opioid receptors, including KOR, have been tested in MDD before, with limited success. If Navacaprant acts through a similar mechanism, there is no compelling reason to expect significantly better efficacy. Second, the preclinical and early clinical data on Navacaprant in MDD are not strongly predictive, given what we view as poor statistical and study design choices. In other words, we believe that the preclinical rationale is not sufficiently indicative of human outcomes, and that the phase 2 study was poorly designed, making it likely that the findings will regress in larger phase 3 trials. Lastly, targeting the opioid system poses regulatory challenges, given the potential for abuse and the associated scrutiny from regulatory agencies (although the company reported that FDA believes there is “no need for the Company to conduct further studies to assess physical dependence with navacaprant”). Regarding BPD, we similarly believe that Navacaprant is unlikely to demonstrate significant efficacy to support approval. While major depressive episodes in BPD share many clinical features with MDD, our doubts about Navacaprant’s efficacy in MDD extend to its use in BPD. Moreover, BPD is a notoriously difficult area for drug discovery, as any antidepressant effect must be carefully balanced to avoid triggering manic episodes. While some antidepressants are used off-label in BPD, mood stabilizers remain the primary treatment option due to these challenges. Furthermore, the remaining assets in the company’s pipeline are not highly promising. The schizophrenia drug (M4 receptor agonist) was recently halted due to toxicity concerns. The AD agitation drug targeting vasopressin is a long shot, considering the lack of clear evidence of the physiological relationship. The remaining drugs are in the very early phases of clinical development and are years away from potentially entering the market; hence, we will not discuss them in detail, as they likely account for only a small fraction of the current market capitalization. Pipeline and Clinical Trials Neumora’s pipeline currently includes three drugs in the clinical phase and five drugs in the preclinical phase. The clinical drugs include Navacaprant, the KOR antagonist currently undergoing phase 3 testing in MDD and phase 2 testing in BPD; NMRA-511, a V1aR antagonist currently in phase 1 testing for AD agitation; and NMRA-266, an M4 agonist that was designed to treat schizophrenia but is currently on clinical hold. We will not discuss preclinical assets, as they are far from material compared to late-stage development assets. Neumora Corporate Presentation - Pipeline Neumora also provides an overview of the ongoing trials related to the lead asset Navacaprant. The MDD program involves three phase 3 trials: KOASTAL-1, KOASTAL-2, and KOASTAL-3. Additionally, the company is conducting an open-label expansion trial, to evaluate long-term safety and efficacy of the drug (KOASTAL-LT). The BPD program currently involves one phase 2 trial. Neumora Corporate Presentation - Navacaprant Program KOR & MDD/BPD Major depressive disorder (MDD) is a pathological neuropsychiatric condition characterized by emotional symptoms (e.g., depressed mood, anhedonia), neurovegetative symptoms (e.g., fatigue, sleep disturbances, weight changes), and neurocognitive symptoms (e.g., difficulty concentrating, agitation, psychomotor retardation) (Marx et. al. 2023). MDD affects approximately 185 million people globally, with an annual prevalence of about 5%. Diagnosis is typically based on the DSM-5-TR or ICD-11 criteria. The etiology of MDD is complex and not entirely understood, but it is believed to involve a combination of genetic, environmental, psychological, and biological factors (Rasic et. al. 2014) (WHO, 2022) (Miller et. al. 2016). Most antidepressants target the monoaminergic pathways, and treatment commonly starts with selective serotonin reuptake inhibitors (SSRIs) or serotonin-norepinephrine reuptake inhibitors (SNRIs). Other classes include tricyclic antidepressants and atypical antidepressants. Despite the variety of options, approximately 66% of patients do not respond to initial treatment, and up to 15-30% develop treatment-resistant depression (defined as a lack of response to two successive lines of therapy) (Dodd et. al. 2021). Bipolar disorder (BPD) shares certain similarities with MDD, but, in stark contrast, depressive states alternate with manic episodes, hence its classification as a biphasic disorder (Vieta et. al. 2018). BPD is an umbrella term that includes bipolar I, bipolar II (which Neumora focuses on), cyclothymia, and atypical subtypes. Subclassification depends on the severity and duration of depressive or manic episodes. Consequently, the BPD patient population is highly heterogeneous. The primary goal of pharmacological treatment in BPD is to manage acute depressive or manic episodes and achieve stable remission (Fountoulakis et. al. 2016). Only three drugs have been approved by the FDA for treatment of bipolar depression, all of which are antipsychotics that stabilize mood (Lurasidone, Olanzapine-fluoxetine, Quetiapine). The risk of using antidepressants to treat acute bipolar depression is the potential for mood switching to hypomanic or manic states (Pacchiarotti et. al. 2013). While some antidepressants are used off-label in BPD, mood stabilizers are typically the mainstay of treatment. The connection between the opioid system and depressive symptoms dates back to the mid-19th century, though use of opioids for mood disorders diminished significantly by the mid-20th century due to the emergence of widespread opioid abuse, dependency, restrictive legislation, and the availability of non-addictive antidepressants (Jelen et. al. 2022). More recently, there has been growing evidence supporting a role for the opioid system in the dysregulation of mood associated with depression. There are four opioid receptors expressed in the human body: the mu (MOR), kappa (KOR), delta (DOR), and nociceptin (NOP) receptors (Benarroch 2012) (Al-Hasani et. al. 2011). These receptors are structurally similar and belong to the class of G protein-coupled receptors (GPCRs). They exhibit inhibitory effects, and when activated, they suppress the downstream formation of cyclic adenosine monophosphate (cAMP). This inhibitory property and the resulting protein-protein interactions are believed to influence neuronal excitability through alterations in calcium and potassium permeability, effectively reducing neuronal activity. The MOR, which is functionally characterized by its analgesic and sedative properties, has beta-endorphin as its primary endogenous ligand and presents considerable challenges in drug development due to its known association with addiction (Waldhoer et. al. 2004). KORs, primarily activated by a class of peptides called dynorphins, have a role in stress and dysphoria, whereas DORs are mostly bound by enkephalins. Importantly, these peptides exhibit partial agonistic properties across different opioid receptors (except for NOP). The NOP receptor is activated by nociceptin/orphanin FQ, which has distinct physiological effects from the classical opioid peptides. Navacaprant & other KOR ligands (Aticaprant, Buprenorphine, JDTic, Icalcaprant, PF-4455242, Salvinorin A)  The KOR space has seen its fair share of trials in MDD for both antagonists and agonists. We intend to briefly overview the history, rationale, and efficacy of some of the other KOR-targeting compounds in MDD, compare them to the available data for Navacaprant, and extrapolate the results to future studies to understand whether the drug is positioned for regulatory success. Rational (Preclinical Data) The role of KOR has been extensively studied, and while there is still ongoing debate regarding the exact functional profile of this receptor system, it is generally accepted that KOR agonism plays a role in pro-depressive behaviors (Pecina et. al. 2018). The major symptoms associated with stimulation of the receptor in rodent models involved increased dysphoria, anhedonia, and aversion, which were often rescued in the context of chronic stress animal models (Jacobson et. al. 2020). Administration of KOR antagonists resulted in improvements in stress-related behaviors, social interaction deficits, and increased sucrose preference (generally considered a surrogate for anhedonia). In addition, KOR antagonists, unlike MOR agonists, did not produce acutely rewarding effects in rodent models, and hence could possibly be devoid of potential abuse liability (Valenza et. al. 2017). A variety of KOR ligands have been studied in vitro and in vivo; however, none have achieved regulatory success to date. Notable compounds include Aticaprant, Buprenorphine, JDTic, Icalcaprant, PF-4455242, and Salvinorin A. Since all these compounds (except Salvinorin A, which is an agonist) are KOR antagonists, albeit with varying affinities for different opioid receptors, we generally assume that they should elicit similar functional efficacy in clinical trials (i.e. bind to the receptor and block the endogenous ligand). This is a bold assumption; however, as we intend to show, Aticaprant and Navacaprant, despite minor differences in their pharmacodynamic/pharmacokinetic profiles, result in similar target binding at their effective doses, which vary by a factor of eight (10 mg vs. 80 mg)! ChemDraw - Molecular structure of three KOR antagonists - Navacaprant, Aticaprant, and JDTic Buprenorphine, discovered in the 1960s, is an older compound with both partial MOR agonist and KOR antagonist properties and has been extensively tested for MDD (Riblet et. al. 2023). Due to Buprenorphine's partial agonistic nature and its associated potential for abuse, it was subsequently combined with a potent MOR antagonist, Samidorphan (BUP/SAM or ALKS-5461), for further clinical testing in MDD. JDTic is a long-acting KOR antagonist initially developed by Eli Lilly. Its development was halted during a phase 1 trial for cocaine abuse due to safety concerns involving potentially life-threatening arrhythmias (Buda et. al. 2015). This prompted further interest in developing short-acting KOR antagonists, such as Aticaprant (LY- LY-2456302) (Urbano et. al. 2014). PF-4455242, another Pfizer-developed compound from the same period as JDTic, was discontinued during phase 1 trials due to unfavorable toxicity in animals exposed to the drug for three months (Li et. al. 2016). Icalcaprant was developed by Pfizer and later licensed to Cerevel Therapeutics, which later got acquired by AbbVie (AbbVie 2024). Finally, Salvinorin A, a KOR agonist, is being developed by atai Life Sciences for use in various mental health conditions, including treatment-resistant depression (Jelen et. al. 2022). Infra, we compare the reported receptor affinities of these compounds and their corresponding IC50 values (concentration of a particular inhibitory drug needed to inhibit, in vitro, a given process or component by 50%) (Guerrero et. al. 2019) (Carroll et. al. 2004) (Bidlack et. al. 2018) (Suidan et. al. 2022). Notably, most of these ligands exhibit similar sub-nanomolar concentration values.   Receptor affinity [nM] / IC50 [nM] Navacaprant Aticaprant Buprenorphine JDTic Icalcaprant PF-4455242 Salvinorin A   KOR 1.5 / 0.8 0.8 / 0.8 2.5 / 0.71 0.031 / 0.1 1.47 / 0.042 3.0 2.4   MOR 450 / 110 24 / 17.4 1.5 / 0.63 10.5 / 6.58 46.2 / 9.1 64 -   DOR NA / 6500 155 / 110 6.1 / 4.2 153 / 168 - >4000 -   Click to enlarge Most relevant to the evaluation of Navacaprant is Aticaprant, as it has shown a generally favorable safety profile and has undergone a similar level of clinical investigation. Available data include pharmacokinetic/pharmacodynamic (PK/PD) assessments, forced swim tests (in mice), and sucrose preference tests (in mice), with most findings being statistically significant (Wong et. al. 2024). However, it is important to note that while these findings are statistically significant, animal behavior models are known to be poorly predictive of clinical outcomes in humans, especially in neuropsychiatric field (Nestler et. al. 2010). The forced swim test, for example, lacks face (i.e. how the model recapitulates important anatomical/biochemical/pathological or behavioral features of human disease) predictive validity (i.e. how well the model predicts the efficacy of the given treatment in humans); even in animals raised in chronic stress conditions, they are exposed to acute, short-duration stress, and any \"improvement\" is measured only in that brief interval. Sucrose preference tests may better mimic human anhedonia, but they are not specific to depression, as anhedonia is often seen in other conditions, such as schizophrenia and stimulant withdrawal. Furthermore, although results from these tests are statistically significant, they often lack in terms of effect size. For instance, the study by Jacobs et. al (2020) showed a statistically significant improvement in sucrose preference; however, the magnitude of the difference was not particularly impressive. It is also worth considering that animals used in these studies are often dosed at levels far above those in human trials (e.g., 10 mg/kg vs. approximately 0.1 mg/kg), making these findings appear potentially modest when translated to clinical settings.   Jacobs et. al 2020 - Sucrose Preference Vehicle vs. Aticaprant Navacaprant's preclinical data included PK/PD studies, tail-flick assays (using Sprague-Dawley rats), and cognitive assessments in macaques (Wallace et. al. 2022) (Wong et. al. 2024). The tail-flick assay, a measure of nociception, has little relevance to MDD, and the findings were non-significant. Similarly, the study involving macaques assessed working memory using the Wisconsin General Testing Apparatus, but this also has limited relevance to MDD, as the stress-induced state was acutely triggered by a pharmacological agent, also yielding non-significant results. It is important to note that the lack or presence of statistical significance, or even the magnitude of effect size, is not necessarily an argument against the potential efficacy of Navacaprant (we will address this further in the clinical data section). Rather, it highlights the level of evidence that should be required to consider a therapeutic agent in neuropsychology as “promising.” Assays such as the forced swim test or tail suspension test were developed decades ago for screening potential antidepressants (Nestler et. al. 2010). Early antidepressants were discovered by chance, and these behavioral screens were subsequently designed to detect compounds that could mimic the efficacy of existing reference drugs (Ban 2006). However, these screens were never validated to reflect pathophysiological processes related to the drug's mechanism of action. This likely contributes to the persistently low success rates and stagnation in neuropsychiatric treatment development over the decades, as many researchers continue to rely on these screens, despite their limited predictive value. Clinical Data In the context of clinical efficacy, we will focus on three molecules: Buprenorphine (combined with Samidorphan, referred to as ALKS-5461), Aticaprant, and Navacaprant. ALKS-5461 ALKS-5461, a combination of Buprenorphine (a MOR agonist and KOR antagonist) and Samidorphan (a MOR antagonist), was the first KOR-antagonistic treatment regimen considered for approval in MDD. Despite the \"promising\" data reported by the sponsor and the FDA's fast-track designation, the advisory committee (ADCOM) voted 21-2 against the drug's approval. Notably, the FDA's main concerns centered around the drug's efficacy, with one of the opening slides clearly reflecting their disagreement with the efficacy claims. FDA ADCOM presentation ALKS-5461 A meta-analysis of Buprenorphine (BUP) and Buprenorphine/Samidorphan (BUP/SAM) efficacy in MDD found a lack of significant benefit, which included data from a 2019 phase 3 trial of ALKS-5461, reporting a non-significant difference in MADRS score change (-4.8 vs. -4.6) (Riblet et. al. 2023) (Zajecka et. al. 2019). The forest plot presented infra illustrates that early-stage trials with small sample sizes often tend to overstate efficacy, a trend that is, perhaps unsurprisingly, evident here. Riblet et. al. 2023 - Buprenorphine in MDD and TRD  Without going into excessive detail, the point is that ALKS-5461 did not secure regulatory approval, and the efficacy reported across multiple trials was generally modest or absent. While the safety profile of ALKS-5461 should not be directly extrapolated to Navacaprant due to differing binding affinities, the efficacy in terms of KOR binding and receptor occupancy may be comparable. If the efficacy exists at all, there should be some detectable improvement, even at a low dose. The dosing of ALKS-5461 was considerably lower than that of Navacaprant, however, as we will discuss infra, the dosing of Navacaprant and Aticaprant suggests that this difference may not be necessarily relevant. Aticaprant Aticaprant is the closest competitor to Navacaprant, and the sponsor chose to include a side-by-side comparison of both drugs in their corporate presentation. Neumora Corporate Presentation - Navacaprant vs. Aticaprant One major difference between Navacaprant and Aticaprant is their primary indications: Aticaprant is being investigated as an adjunctive treatment to standard-of-care (SOC) therapy (SSRI/SNRI) at a 10 mg dose, whereas Navacaprant is being tested as a monotherapy at 80 mg. Both drugs have undergone one phase 2 trial and are now being tested in phase 3 trials. The sponsor emphasizes the binding selectivity of KOR over MOR, which is ten times greater for Navacaprant. This distinction is argued to potentially reduce target-related toxicity. However, despite differences in dosing and binding selectivity, both drugs are estimated to achieve approximately 90% KOR coverage for about 24 hours, with a similar half-life of around 30 hours.[KP1] In fact, some basic pharmacology considerations can largely explain why, despite the large difference in doses, the drugs seem to have the same functional profile. The reported animal studies showed a brain-to-plasma ratio of approximately 2 (Guerrero et. al. 2019). Human plasma concentration data reported by Neumora indicates a peak concentration of roughly 30 ng/mL for the 80 mg dose. This corresponds to an estimated 60 nM exposure in the tissue of interest—approximately 75 times the IC50 value reported in the literature. Neumora Navacaprant Human PK poster Assuming a comparable brain-to-plasma ratio for Aticaprant, a dose of 10 mg results in a peak plasma concentration of approximately 15 ng/mL in healthy humans. Consequently, the estimated exposure at the tissue of interest would be around 30 nM, representing roughly 38 times the IC50 value for Aticaprant. Unsurprisingly, whether the exposure is 38 times or 75 times the IC50, receptor saturation is likely near maximum in both cases. Naganawa et. al 2016 Aticaprant Human PK  The sponsor contrasts the clinical findings of both drugs, presenting significant findings for Navacaprant while showing a mix of significant and non-significant findings for Aticaprant. In our opinion, this comparison is misleading and flawed (one can think of it as statistics apples and oranges). The significant findings for Navacaprant were calculated in a subset of the population using inferior statistical methods (Last observation carried forward), while the Aticaprant data is reported for the full intention-to-treat (fITT) population, which had a greater response than the initially planned enriched intention-to-treat (eITT) population (Lachin 2016). Furthermore, the p-values for Navacaprant were reported for one-sided tests with a 0.2 alpha, potentially leading to an exacerbated chance of a false positive finding. Aticaprant, like Navacaprant, is currently undergoing phase 3 testing, and prior phase 2 data has been reported in Nature Neuropsychopharmacology (Schmidt et. al. 2024). Since Aticaprant is tested in an adjunctive setting, it is administered alongside SOC SSRI/SNRI therapy at one-eighth of the Navacaprant dose. The trial was designed as a sequential parallel comparison, similar to the study 202 of ALKS-5461, which was critiqued by the FDA. The design allowed for two analyses: the entire population and an \"enriched\" population, which excluded patients who responded to placebo during the lead-in period. The expectation was that the enriched cohort would be more sensitive to detecting the drug's efficacy, as it excluded those with a strong placebo response. Contrary to the sponsor's hypothesis, which assumed a potential greater effect size in the enriched population, the data showed the opposite. \"The treatment effect in the fITT analysis dataset exceeded that in the eITT analysis dataset. A larger treatment effect and effect size had been predicted for the eITT analysis dataset that included only participants who were non-responders during the placebo lead-in period.\" Although the trial reported some statistically significant findings, we remain highly skeptical, owing to the one-sided 80% confidence intervals, which as we mentioned supra could be suggestive of a high chance of type I error. The change in MADRS scores from baseline is shown in the figure below. Schmidt et. al. 2024 - Aticaprant Efficacy  The difference between placebo and drug on the MADRS scale at week 6 was -2.1 [1.25] in the eITT population and -3.1 [1.05] in the fITT population. The perceived additional efficacy of Aticaprant on top of SOC is modest at best. Given the smaller effect size observed in the eITT population and the potential exacerbation of type I error (with an alpha set at 0.2, implying a 20%+ chance of false positive), we suspect the efficacy might be close to non-existent. Furthermore, estimates of the minimal clinically important difference (MCID) for MADRS in MDD range from 3 to 9 points, a topic that has been widely debated (Leucht et. al. 2017) (Turkoz et. al. 2021). This means that, in the best-case scenario based on phase 2 data, Aticaprant would barely reach the lower end of the MCID range, accompanied by a greater incidence of adverse events. One could argue that the sliver of efficacy that seems to be present on the figure comes from the placebo effect of an active drug (perception of being allocated the drug due to side effects), previously suggested by Dr. Vinay Prasad. Simply put, we find the phase 2 trial data unconvincing. Navacaprant Unlike Aticaprant, Navacaprant's clinical data has never been fully published in a scientific journal. The currently available data originates from a phase 2 trial initiated by BlackThorn, which was subsequently modified and analyzed by Neumora (presented at ACNP 2023 annual meeting by Dr. Sanjay Mathew). In our opinion, the trial had multiple design flaws and statistic pitfalls, which significantly limits its predictive value regarding phase 3 success. In short, we believe that the data has been poorly analyzed to produce a sign of statistical significance (in a study “not powered for significance”) and, in our view, will have no bearing on future readouts. We aim to demonstrate this point in the following sections. The phase 2 trial (NCT04221230), initially launched by BlackThorn, was originally intended to enroll patients with mild to moderate MDD (HAMD-17 total score 14-22). However, following Neumora's acquisition of BlackThorn, the protocol was amended to expand the inclusion criteria to also encompass patients with severe MDD (HAMD-17 score ≥22). At that time, the statistical analysis plan was also amended and, as the sponsor emphasizes, pre-specified. The trial ultimately enrolled 204 patients, of whom 100 had a baseline HAMD-17 score ≥22. The primary endpoint was the change in HAMD-17 score from baseline to week 8, while secondary endpoints included changes at 4 and 8 weeks in CGI-I, SHAPS, HADS, HADS-A, HADS-D, HAM-A, SDS, and CGI-S. The first glaring issue is the mid-trial change of the study protocol. While protocol changes and amendments in phase 2 exploratory trials “not powered for statistical significance” (often a miss-understood concept), can sometimes be reasonable, it becomes problematic when the sponsor claims the study is designed to estimate effect size while ignoring the increased risk of type I error associated with additional statistical comparisons and post-hoc analyses, especially without a plan to control for multiplicity testing (a point that FDA and AdComs often make). As shown in infra, the primary endpoint of change at 8 weeks in HAMD-17, calculated using mixed model for repeated measures (MMRM), was not statistically significant. ACNP2023 presentation Navacaprant Efficacy  The sponsor argued that, in accordance with a pre-specified statistical plan, since more than 10% of patients had missing data, the analysis used the last observation carried forward (LOCF) method, and the 8-week difference was reported as statistically significant at p=0.0204. The \"10% discontinuation threshold\" appears modest, given that discontinuation rates reached nearly 40% in the placebo arm and nearly 30% in the drug arm. Neumora conducted another pre-specified comparison to assess efficacy in patients with a baseline HAMD-17 score >22. In this subgroup, the MMRM method was not used at all; instead, LOCF estimated the difference between placebo and the drug at -2.8, with a p-value of 0.037. Barely significant, however, considering that the primary endpoint failed, if one were to assume hierarchical hypothesis testing (or account for multiplicity in any way), all later comparisons would be futile and inconclusive anyway. It is important to note that \"pre-specified\" does not equate to \"well-designed\" or \"justified.\" LOCF is widely recognized as an inferior statistical method, known for its tendency to overestimate efficacy. Additionally, a 30-40% dropout rate is exceptionally high, rendering any conclusions about drug efficacy even more speculative. The number of comparisons made, combined with inferior statistical methods, elevates the likelihood of type I error—especially considering that the primary endpoint at 8 weeks was not statistically significant when calculated using MMRM. The findings related to the SHAPS scale appear more promising, as the only reported figure indicated a statistically significant difference of -4.8 points, which is so inconsistent with the Aticaprant data (difference of 0.8), that it seems implausible (do not confuse with impossible).   Neumora 10K - Navacaprant Efficacy  However, this figure, like the statistically significant change in HAMD-17 total score, was reported in the HAMD-17 >22 subpopulation. As previously mentioned, although pre-specified, this comparison is flawed. While one might be tempted to interpret all the mentioned results as data coming from a phase 2 trial that enrolled 204 patients, the actual data (i.e., data not imputed using the LOCF method) presented in this figure includes only about 60 patients. With approximately 30 patients per arm, the dataset resembles an early exploratory phase 1 study far more than a phase 2 trial intended to guide the design of subsequent phase 3 trials. Furthermore, the choice of >22 as the cutoff for \"severe\" depression seems arbitrary, as different sources set the \"moderate-to-severe\" threshold at various levels—some at 17, others at 20 (Leucht et. al. 2018) (Hamilton 1960). The secondary endpoints reported by the sponsor include a range of metrics that we mentioned at the beginning of this discussion. Neumora 10K - Secondary Measures Navacaprant Efficacy  Some secondary endpoints were statistically significant, while others were not. Notably, the clinician-oriented endpoint, CGI-I, showed no statistical significance at either week 4 or week 8. Additionally, although HADS-A was listed as a pre-specified secondary endpoint on clinicaltrials.gov, the company did not report any findings for this measure. As of this writing, we were unable to determine why this result was omitted. An important issue in the study design, beyond the efficacy measures, concerns the choice of inclusion and exclusion criteria. The sponsor chose to exclude patients with a history of substance or alcohol use disorder within the past year, despite the fact that nearly 33% of individuals with MDD may experience such co-morbidities (Davis et. al. 2008). This criterion, along with others related to general health, likely resulted in a patient population that is far from a generic participant of a large phase 3 MDD trial, not to mention an average MDD patient in clinical practice. The enrolled patients were slightly older, with a mean age of approximately 42, and there was a small imbalance in the proportion of Black/African American participants between the treatment arms. Furthermore, the stringent inclusion criteria led to a high exclusion rate, with around 68% of screened patients failing to enroll (645 screened, 204 enrolled). The safety profile presents another anomaly, with the rate of treatment-emergent adverse events reported as 35% in the drug arm and 44% in the placebo arm. Neumora Corporate presentation AE incidence Navacaprant  Most adverse events were balanced between groups; however, the incidence of nausea was higher in the treatment group (4.9%). This finding is surprising, as efficacious drugs often exhibit more side effects, which typically mean that the drug is pharmacologically active (i.e., not inert). TEAE leading to discontinuation occurred in 12% of placebo subjects and only 1% of the Navacaprant subjects. This is odd, considering that the healthy volunteer study reported a higher incidence of adverse effects in the high-dose groups compared to the incidence observed in the phase 2 trial, albeit with a smaller sample size. Neumora presentation of healthy volunteers study data - AE For instance, the incidence of headaches reported in the phase 2 trial was 4.9%, whereas, in the healthy volunteer study, it stood at 8% across all drug doses and 17% in the 80 mg arm, while the placebo arm experienced a 0% incidence. It is entirely possible that Navacaprant is generally safe, potentially safer than other KOR antagonists, however the inconsistency between the phase 1 and 2 data, and the greater fraction of patients experiencing adverse events leading to discontinuation in the placebo arm is strange. Overall, the quality of evidence for Navacaprant is poor, but the crucial question remains—can these results be replicated in the phase 3 trials? Given the available information, this seems unlikely. The phase 3 trials have numerous design differences compared to phase 2. For one, the inclusion and exclusion criteria for the phase 3 trials are less stringent, which means the enrolled population will likely be different from that of the phase 2 trials. Notably, despite the emphasis on anhedonia in the drug rational and phase 2 trial, the phase 3 trial dropped the requirement of prominent anhedonia at screening.   Criteria Phase 2 Trial Phase 3 Trial   Age Range 18-65 years 18+ years   Primary Diagnosis MDD with prominent anhedonia confirmed by SCID-5-CT MDD without psychotic features confirmed by SCID-5-CT   Duration of Current Episode 3 weeks to 12 months 4 weeks to 12 months   Change in Depression Scale Scores ≤3-point change in HAMD 17 between screening and baseline ≤20% change in MADRS score between screening and baseline   Body Mass Index (BMI) 18-40 kg/m² Not specified   Stability of Medical Condition Medically stable based on medical history, vital signs, labs, and ECG General medical stability required, but less specific   Contraceptive Use Specific contraceptive requirements for men and women Not mentioned   Substance Use Disorder History No substance/alcohol use disorder in the past year No moderate-severe substance/alcohol use disorder in the past year   Suicidality Excludes active suicidal intent or plan, or recent attempts Excludes active suicidal intent or plan, or recent hospitalization   Medical Conditions and Labs Must be free from major medical conditions affecting safety or compliance; excludes hepatic, renal, and significant cardiac disease Excludes participants with major medical conditions and abnormal labs; no detailed condition list   Exclusionary Medications No psychoactive medications; no CYP3A4 modulators; specific medication restrictions Restrictions on psychotropic medications; less detail on other medications   Click to enlarge Additionally, the primary endpoint has been changed from HAMD-17 to MADRS, complicating the translation of phase 2 results to phase 3 outcomes. Importantly, the phase 3 trials will utilize MMRM analysis instead of LOCF, which despite its tendency to over-estimate effect size had already shown some non-significant findings in the phase 2 analysis. If we take the observed phase 2 HAMD-17 effect size (mean change of -7.1 in placebo vs. -9.9 in the treatment arm), and use the sample size formula for statistical superiority:   Sample size formula assuming a standard deviation (s) of 8, the estimated sample size per arm comes down to 128 patients—160 less a potential 20% attrition rate (or 25% of 128). This means the 320-patient phase 3 trial was likely designed based on an assumed ~2.8-point difference in HAMD-17 between the drug and placebo. Since HAMD-17 is correlated with MADRS, with similar fractional changes from baseline, a -2.8 delta on HAMD-17 might correspond to a slightly greater MADRS change, making it seemingly easier to detect a difference on MADRS (e.g., a -3 change on HAMD-17 could correspond to approximately -4 on MADRS). By that metric, the study appears to be well-powered. However, this interpretation is overly simplistic. The primary reason for why assuming a -2.8 delta on HAMD-17 is misguided, is the flawed study design and statistical analysis of the phase 2 trial. As previously stated, the effect size is likely overestimated. Even the most basic argument undermines the -2.8 estimate—multiplicity testing issues aside—the value was calculated using the LOCF method, whereas the phase 3 trial will use the MMRM method. Since LOCF tends to overestimate effect size, the MMRM estimate will almost certainly be the same or smaller. If we conservatively assume a HAMD-17 delta closer to what was observed in the primary endpoint analysis (-1.7, rounded to -2), the approximate change in MADRS would be also around -2 points. To detect such a change using the same equation, a sample size of 392 participants per arm would be needed (s=10). This clearly indicates that if we assume a more realistic effect size, the phase 3 trial is unlikely to achieve statistical significance—and that is a good thing, as any change below 3 points on the MADRS scale is not considered clinically meaningful anyways. A quick t-test calculation shows that a 2-point difference of MADRS under the assumptions of the phase 3 trial yield a p-value of 0.11. Two more arguments suggest that the effect size will shrink further. First, more broadly speaking, regression to the mean typically occurs as trials move from phase 2 to phase 3, as evidenced by the ALKS-5461 data presented earlier. Second, if we assume similar efficacy between Aticaprant and Navacaprant, the delta in the Aticaprant phase 2 study was approximately -2 on the MADRS scale (similar to delta observed in the primary analysis of Navacaprant phase 2), which would not be statistically significant under conventional statistical analysis. We want to clarify our interpretation of \"lack\" or \"evidence\" of efficacy. It is understood that the absence of statistical significance cannot be equated with a definitive lack of “effect.” We are not arguing that Navacaprant, Aticaprant, or any other KOR ligand has no effect. These molecules bind to receptors and may indeed inhibit downstream neuronal pathways, thereby altering brain activity. However, when we refer to a \"lack\" of efficacy, we mean that the drug does not demonstrate enough of an effect to be considered a viable treatment option. There could be myriad reasons why a molecule like Navacaprant might not be an effective therapeutic in MDD. For example, its KOR antagonism may be functionally counteracted by the activity of other receptors, or KOR activity might be just one of many factors—perhaps one out of a hundred—that contribute to depression. There are countless possible explanations. Thus, when we say that the drug lacks profound efficacy, we do not mean that it has no effect whatsoever; rather, we mean that the effect it has is insufficient for it to be considered a good treatment. To summarize, we have argued that the data used to inform the Navacaprant phase 3 trials is weakly predictive. We believe that the upcoming phase 3 readouts will confirm our thesis, with the first results expected in the coming months. Where did the AI go? A savvy investor might notice a key detail initially brought up in the introduction that was conspicuously absent throughout the rest of the article. One major claim made by the sponsor is missing: where did all the biomarker-data-driven algorithms designed to \"solve\" heterogeneity go? According to Neumora's 2021 press release: “[Neumora Launches to Pioneer a New Era of Precision Medicines for Brain Diseases by] redefining neuroscience research and development with a data-driven precision neuroscience platform to develop targeted therapies for brain diseases.” “[…] data science platform aims to cut through brain disease heterogeneity to match the right patient populations to targeted therapeutics.” We recommend that readers review the entire press release to assess whether any of these data-driven claims have materialized in the company's operations. The phase 2 trial was designed using generic inclusion and exclusion criteria—numerous, yet nothing extraordinary. The phase 3 trial appears even less specific, with typical, broad inclusion and exclusion criteria comparable to any other phase 3 trial in MDD. We expect the population to be as heterogeneous as that of any other MDD phase 3 trial. At the time of writing, it seems that the technical aspects of Neumora's platform have not been effectively utilized to inform the design of Navacaprant's clinical trials. The sponsor claims that \"traditional neuroscience R&D is constrained by a ‘one-size-fits-all’ treatment approach, often leading to underwhelming efficacy,\" and that \"Neumora aims to overcome the historical challenges of neuroscience R&D by targeting enriched patient populations.\" Yet, they proceed to run a phase 3 trial with a generic MDD population, relying on what we consider mediocre prior evidence of efficacy. Valuation, Probability of Success, and Cash Burn Valuation  MDD and BPD are sizable markets, with annual revenues of $10B+, and novel drugs can easily gross hundreds of millions, if not billions, of revenue. When modelling the value of Navacaprant, which we argue makes up 80%+ of the current valuation of the company, we will pay little attention to BPD, since the indication lacks phase 2 data (we assume probability of success =<10%). We will assess the value of Navacaprant in MDD and BPD patients using epidemiologic data, forecasting, and available data from current historic sales of antidepressants. The sponsor provides an estimate of addressable population in MDD of 11M, of which they expect to access ~3-5%. Notably, the largest population, first line patients, are doubtful to use Navacaprant, considering that the clinical trials lack an active comparator arm, and to date, the effect sizes seem comparable or lower than those of generic SOC therapies. To remain conservative, we will take the 11M population at face value, however, owing to the high attrition rates, adherence was modelled below industry average. ME - NPV model  We modeled the entry of Navacaprant into the US MDD market in 2026 and 2028 into the US BPD market. The sales projection includes a typical five-year ramp-up period and a 4% market penetration rate (mid-value from the 3-5% range provided by the sponsor). The adherence rate was modeled at 50, based on historical data reported in the literature (Dell’Osso et. al. 2020). The Average Selling Price (ASP) was modeled at 75% of the Average Wholesale Price (AWP). The initial annual cost in the US was set at $10,000, which is in line with costs associated with novel treatments for MDD (f. ex. SPRAVATO), with a 2% annual price increase consistent with inflation. We also account for an obligation of low-single digit percentage royalties on net worldwide sales owed to TSRI, in accordance with the license agreement. The percent values were primarily adapted from research published in David et al. 2017. Cost of Goods Sold (COGS) was modeled at 10% of revenue, while R&D expenses were kept constant at $20 million per year to cover developmental costs associated with ongoing drug research post-market entry. Selling, General, and Administrative (SG&A) expenses were set at 34% of revenue. With a peak revenue of approximately ~$3B and assuming a discount rate of 10%, the Net Present Value (NPV) of Navacaprant in MDD and BPD was estimated to be approximately $5B, corresponding to about $35 per share, based on a share count of 160M, a figure which includes cash. This implies that, according to our model, the market is pricing in slightly less than a 40% probability of success for Navacaprant in depressive disorders. Out modelled peak revenue is ~$2B in MDD and ~$1.2B in BPD, which is largely in line with historic revenues of drugs approved for these indications. Prozac, Zoloft, and Brintellix peaked at sales in the range of $1-3B annually. Probability of Success We believe the true POS for Navacaprant is significantly lower than 40%. In the context of complex, compounded events—such as the success of a KOR antagonist in MDD—we follow the general framework presented by Lau et. al. 2015. We believe there are two necessary conditions (events A, B) that must hold true for Navacaprant to be successful therapeutic for MDD (event C). Thus, we find it justified to analyze the POS of Navacaprant under the concept of “conjunctive low probabilities.” Condition A – Aticaprant at a 10 mg Dose is Functionally Distinct from Navacaprant at an 80 mg Dose We consider Condition A to be very improbable. The pharmacokinetic/pharmacodynamic data reported by various sources (including Neumora) suggest that both Aticaprant and Navacaprant achieve over 90% occupancy of KOR receptors in the target tissue. Therefore, it is reasonable to conclude that both drugs achieve a similar level of efficacy if the surrogate for it is measured by their ability to effectively bind KOR receptors and potentially alter neuronal signaling. This condition is crucial, as in our view, worse efficacy of eITT population in the Aticaprant trial is highly predictive of lack of efficacy. However, to remain conservative, we assign a probability of 30% to Condition A. Condition B – The Phase 2 Navacaprant Primary Endpoint was a False Negative Result The primary endpoint of the phase 2 Navacaprant trial was not statistically significant and, even when considered at face value, was far from clinically significant (a 1.7-point difference on the HAMD-17 scale). If we are to believe that Navacaprant is more efficacious than a sugar pill, we must view this “failure” as a statistical anomaly (random sampling). Assuming a standard power of 80%, the probability of a Type II error should be 20%. Thus, we assign a probability of 20% to Condition B. The probability of event C being positive can be calculated using the following formula: POS This analysis suggests that Navacaprant has an exceedingly low probability of achieving clinical success, estimated at ~6%. To adopt a more conservative stance, considering the upcoming phase 3 readouts are the first of three trials and that anticipated effect sizes vary between phase 2 and phase 3 (seems that not by much though), we set the current POS at 10%. Furthermore, we assigned industry average valuations to the early-stage pipeline products based on their development phase—$100M for phase 1—adding a total of $100M to our forecast. This results in a fair valuation of approximately $700M (or ~5$ per share), which represents a 69% discount to the current market valuation. Cash Burn Historically, Neumora has been operating at a loss, and by the end of Q2, the company had approximately $370 million in cash. Its early-stage pipeline, combined with ongoing late-stage clinical trials, translates into sizable R&D expenses. Between 2020 (the company's inception) and 2023, the total fiscal year (FY) R&D expenses were $17 million, $55 million, $91 million, and $142 million, respectively. General and administrative (G&A) expenses also increased significantly over this period, standing at $8 million, $24 million, $31 million, and $45 million, driven by a rising headcount and expanded operations. The R&D expenses increased by over 50% from 2022 to 2023, primarily due to the ramp-up in clinical programs, including a nearly 400% increase in costs related to Navacaprant developments. Considering R&D and G&A expenses of approximately $94 million and $29 million, respectively, for the first half of 2024, we project that FY 2024 expenses will approach $250 million. As such, we anticipate that the company will end 2024 with approximately $230 million to $260 million in cash, which should leave Neumora well-capitalized to continue its operations through year-end 2025, assuming a flat cash burn rate between 2024 and 2025. Risks The small- to mid-cap biotech sector is inherently volatile, which any informed investor must recognize. Shorting a stock carries the potential for unlimited losses, and investors should mitigate these risks by hedging or limiting position sizes. While the cost of borrowing shares is often a consideration, it is currently marginal for Neumora. The major risk to our thesis is the reliance on the statistical analysis plan and phase 2 trial data of Navacaprant. Should the drug demonstrate profound efficacy and achieve statistical significance on MDD-related clinical measures in phase 3 trials, the stock's valuation could increase significantly. It could be the case that the efficacy of the compound will differ from what was observed prior, but instead of shrinking, it could expand. Additionally, our valuation of the company could be flawed if we have incorrectly modeled estimates or overestimated the importance of Navacaprant relative to other assets in the pipeline. Conclusion In all, we are not optimistic about Neumora's prospects. While the company is sufficiently capitalized to continue the development of its lead asset, the primary issue lies with the asset itself. In our view, Navacaprant has not demonstrated sufficient efficacy, and given the limited success of other KOR antagonists, we believe it is unlikely to yield significant results in the phase 3 trials, let alone receive regulatory approval. Additionally, the design of the phase 3 trials limits the real-world applicability of the drug due to the lack of an active comparator arm. This means that a psychiatrist would be hesitant to prescribe Navacaprant as a first-line treatment, especially considering that the reported MADRS delta does not seem to outperform the SOC (at best, it might match it). This could further limit the sales potential of the drug if it were to secure regulatory approval, thereby dampening future prospects. The lack of consistency in applying data-driven approaches is unsurprising but nonetheless disappointing. While there may be value in the company’s earlier-stage assets, we currently believe that Neumora Therapeutics' stock price is overvalued.  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