Category: 3. Business

Research Background and Commentary Objectives

In traditional Chinese medicine (TCM) practice, clinical decision-making follows the principle of “Pattern Identification and Treatment” (known as “Bian Zheng Lun Zhi” in Chinese).¹ TCM theory posits that a single disease may present with multiple patterns. Using the four diagnostic methods—inspection, listening and smelling, inquiry, and pulse-feeling and palpation—practitioners identify the patient’s specific TCM pattern and guide treatment.² Since these patterns evolve dynamically, herbal or acupuncture prescriptions are adjusted accordingly,³ underscoring the highly individualized nature of TCM practice.

Despite millennia of practical application in China, TCM modalities such as acupuncture⁴ and Chinese herbal medicine⁵ have yet to attain broad global acceptance. This limited recognition largely stems from two factors: the scarcity of high-quality clinical evidence due to methodological flaws and the suboptimal implementation in earlier studies, and an incomplete understanding of their mechanisms of action.^4,5 In non-Asian cultural contexts, TCM encounters additional barriers to integration into mainstream healthcare systems, including unfamiliarity with its theoretical foundations, inconsistent regulatory frameworks, and divergent standards for scientific validation.⁶ Consequently, research efforts have focused on generating robust evidence, accumulating empirical data, and elucidating mechanisms to inform clinical decision-making and policy development.⁷ The rise of evidence-based medicine has offered a potential turning point, encouraging the use of explanatory randomized controlled trials (RCTs)—prioritized as the gold standard for generating high-level evidence³—to evaluate TCM interventions such as compound herbal formulas and acupuncture. Nevertheless, a fundamental tension exists: the standardized interventions and strict variable controls inherent in explanatory RCTs are misaligned with TCM’s individualized approach.^3,8 Thus, while explanatory RCTs provide a structured method for assessing TCM efficacy, they fail to reflect its real-world clinical practice,^1,9 impeding progress in TCM research and international recognition of its therapeutic value.

Hu et al recently addressed this issue in an article published in this journal, proposing a new research paradigm centered on pragmatic RCTs.⁸ Their paradigm emphasizes patient-preference-based randomization to gather real-world evidence. While we acknowledge this innovative contribution, we have reservations about certain aspects of their proposal.

This commentary aims to: (1) critically evaluate the paradigm proposed by Hu et al; (2) discuss whether TCM interventions are better suited to be evaluated in explanatory or pragmatic RCTs ; and (3) introduce an innovative RCT framework developed by our multidisciplinary research team to further reconcile the “Individualization/Standardization Contradiction” in TCM trials.

Divergent Perspectives on Hu et al’s Pragmatic RCT Framework

Hu et al propose a research framework centered on pragmatic RCTs,⁸ positioned in contrast to explanatory RCTs.¹⁰ Both designs have been used in TCM research,³ but differ significantly in attributes and inferential goals (see Table 1).

Table 1 Comparison of Key Features Between Explanatory and Pragmatic RCTs in TCM

Their model emphasizes: (1) patient-preference-based grouping, (2) standard Western medical treatment as the control, and (3) enhanced TCM intervention methods. Although this model demonstrates conceptual novelty and applied relevance, we identify methodological and practical limitations through a comparative framework analysis. This analysis involved evaluating Hu et al’s model against existing explanatory RCT, pragmatic RCT, and patient-preference trial (PPT) framework across dimensions such as research design principles, clinical relevance, and practical feasibility.

Regarding “Grouping by Patient Preferences”

Hu et al advocate for group allocation based on patient preferences in TCM pragmatic RCTs,⁸ representing a conceptual conflation between trial designs. Trials that assign participants according to preference, including partially randomized patient preference (PRPP) designs, fall under the category of PPTs.¹¹ Unlike pragmatic RCTs, PPTs do not adhere to the core principle of random allocation. Although PRPP paradigm incorporate patient preferences while guaranteeing group balance through partial randomization,¹² their primary aim is to improve recruitment and compliance and to minimize the influence of patients’ preferences on study outcomes¹³—an objective distinct from that of pragmatic RCTs, which focus on estimating intervention effectiveness in real-world settings.¹⁴

We acknowledge that most TCM RCTs are conducted in TCM hospitals, where patients often hold strong preferences for TCM therapies,⁸ making recruitment particularly challenging.¹² To address this, we recommend a phased methodological approach: initial implementation of a rigorously designed single-center trial within a TCM hospital setting, employing proactive management of patient preferences through transparent communication, waitlist-controlled designs, or guaranteed post-trial TCM access for control group participants. Our previous trial experience confirms that this approach enables feasible participant recruitment without substantial delays, even among populations with treatment preferences. Following demonstration of feasibility, safety, and preliminary efficacy outcomes, subsequent expansion to multicenter trials incorporating diverse settings (including Western medicine hospitals and community health centers) would enhance generalizability and minimize selection bias. This sequential methodology ensures practical implementation while maintaining alignment with the ultimate objective of conducting methodologically rigorous and broadly representative RCTs.

Regarding “Control with Standard Western Medical Treatment”

While we endorse Hu et al’s recommendation to use standard Western medical treatment as control in pragmatic RCTs,⁸ their supporting citation requires careful consideration. The cited RCT regarding electro-acupuncture for female stress urinary incontinence (SUI) employed a Streitberger-like placebo needle¹⁵—contradicts their argument.⁸ Such placebo devices^15,16 do not constitute standard Western care control. Evidence-based treatments for female SUI include pelvic floor exercises (Kegels), topical estrogen, neuromodulation, and/or surgical options such as sling procedures or urethropexy.¹⁷

Regarding “Enhancing TCM Intervention Methods”

Hu et al propose converting TCM decoctions into granules or capsules to address challenges such as preparation time, storage challenges, and strong taste, thereby standardizing interventions and reducing patient burden.⁸ While this perspective holds merit, we offer several counterpoints.

First, encapsulated TCM formulations are typically highly processed commercial Chinese polyherbal preparations (CCPPs) produced by pharmaceutical companies.¹⁸ Unlike individualized decoctions, CCPPs follow fixed formulas, potentially undermining the personalized therapeutic approach central to TCM practice.¹⁹

Second, the use of dispensing granules (single-herb extracts) in clinical trials remains contentious due to ongoing concerns regarding their therapeutic equivalence with traditional decoctions.²⁰ The efficacy discrepancies may stem from chemical differences rooted in divergent preparation methods: Traditional decoctions involve co-boiling herbs, whereas dispensing granules are individually extracted and later combined, potentially altering bioactive profiles.²¹ For instance, a Hong Kong study comparing Sanhuang-Xiexin-Tang in decoction and granule form revealed significant compositional differences, with key compounds (eg, berberine, epiberberine, baicalin, wonogoside and emodin) present only in the traditional decoction.²⁰ Additionally, TCM theory emphasizes herb-herb compatibility achieved through co-decoction, which may enhance efficacy and reduce toxicity—an effect not replicable with single-herb granules.²¹ Whether the lack of this process compromises safety remains unclear.²¹ Therefore, granules may not accurately represent the clinical effects and safety profile of traditional decoctions in TCM trials.

As an alternative, commissioned decoctions may better balance intervention standardization and patient convenience in the trial. Unlike self-prepared decoctions which are labor-intensive and prone to variability affecting therapeutic outcomes,²² commissioned decoction services—typically conducted by certified dispensers using automated decoction equipment and standardized extraction parameters—ensure both consistency and convenience. These decoctions are vacuum-packed under sterile conditions, allowing for improved storage and portability.^22,23 By maintaining fidelity to traditional methods while eliminating subjective preparation factors,²³ commissioned decoctions offer a viable solution for clinical trials without compromising therapeutic integrity.

Explanatory vs Pragmatic RCTs: Which Trial Design Suits TCM Evaluation?

Analysis of ClinicalTrials.gov data (2021–2024) indicates that most registered TCM RCTs employ explanatory RCT designs, adhering to randomization, parallel control, and blinding principles.⁸ The explanatory RCT approach rigorously controls confounders to test causal hypotheses under idealized settings, thereby assessing intervention efficacy.²⁴ However, this design often overlooks the individualized, pattern-based modifications inherent to real-world TCM practice, compromising external validity.²⁴

Pragmatic RCTs enhance the generalizability (external validity) of results through broad eligibility criteria and real-world clinical settings, and systematically address patient-centered outcomes by focusing on clinically meaningful endpoints and patient-reported outcomes (PROs) as core measures (Table 1). However, relying solely on pragmatic RCTs also presents challenges. Specifically, while pragmatic RCTs better reflect clinical reality,²⁴ the frequent absence of placebo controls and blinding procedures obscures the distinction between specific and nonspecific treatment effects.^25,26 This is particularly problematic for acupuncture, whose therapeutic legitimacy remains debated, with critics attributing its effects to placebo.²⁷ Placebo controls additionally mitigate confounding factors such as statistical regression to the mean and spontaneous remission.²⁶ Furthermore, pragmatic RCTs require large sample sizes and extended follow-up to generate reliable evidence, substantially increasing costs.²⁸ These challenges are exacerbated in non-East Asian contexts, where limited familiarity with TCM complicates recruitment and implementation.²⁹

Given these constraints, neither explanatory nor pragmatic RCTs alone suffice for robust TCM evaluation. A sequential design offers a viable alternative:³⁰ an initial explanatory RCT establishes efficacy under controlled conditions, followed by a pragmatic RCT to evaluate real-world effectiveness. This approach positions the pragmatic RCT as a complement to, rather than a substitute for, explanatory RCT, offering a more comprehensive evidence base.^30,31

Beyond sequential application, we propose—primarily for cost-efficiency—integrating explanatory and pragmatic RCT elements into a unified hybrid framework. This model, detailed subsequently, aims to incorporate classical TCM diagnostic and therapeutic features within an RCT structure, capturing both the strengths and weaknesses of TCM interventions from a “real world” perspective.

In addition, several studies have examined trial designs that transcend the traditional “explanatory-pragmatic” dichotomy to better accommodate the inherent complexity of evaluating TCM interventions. Beyond the PRPP designs discussed earlier, other approaches such as cluster RCTs (suitable for clinic-based TCM services),³² N-of-1 trials (appropriate for highly individualized TCM treatments),³³ and adaptive trials (enabling predefined modifications based on interim results, thus facilitating efficient evaluation of multicomponent TCM interventions)³⁴ have been implemented in specific contexts. These methodological innovations underscore that no universally “perfect” trial design exists; rather, design selection or integration should be guided by the research question, intervention characteristics, and implementation context.

TRIPLE-TCM: A Trans-Paradigm Framework for TCM Clinical Trials

To bridge the gap between individualized care and scientific validation in TCM clinical research, our team proposed a conceptual research framework—TRIPLE-TCM (Trans-paradigm Randomized-Individualized-Preference-Linked Efficacy/Effectiveness Evaluation for TCM, see Table 2). This conceptual framework integrates key elements from three established RCT designs: explanatory RCT, pragmatic RCT, and PRPP trial. As outlined in Figure 1, TRIPLE-TCM offers a five-step procedure for trial design and implementation, which was developed through a systematic and multi-stage process. Initially, our core multidisciplinary team—including TCM clinicians, educators, methodologists, and clinical psychologists—identified key methodological challenges in current TCM RCTs through an interdisciplinary workshop. Building on this, we drafted the fundamental components of the TRIPLE-TCM framework based on our team’s prior clinical trial experience and extensive literature review. To ensure the framework’s rigor, generalizability, and feasibility, we expanded the discussion by forming an extended expert consensus panel. In addition to the core team members, this panel included biostatisticians, Western medicine physicians, health economists, evidence-based medicine specialists, health policy makers, clinical research managers, and patient representatives. Through three rounds of online interdisciplinary workshops, the five-step procedure and its core elements were iteratively reviewed and refined, resulting in a high level of consensus.

Table 2 Definition of the “TRIPLE” Acronym

Figure 1 Implementation Process of TRIPLE-TCM. Abbreviations: CHM, Chinese Herbal Medicine; ICER, Incremental Cost-Effectiveness Ratio; ITT, Intention-To-Treat; QALYs, Quality-Adjusted Life Years; QoL, Quality of Life; TCM, Traditional Chinese Medicine.

Step 1: TCM Pattern Identification & Pattern-Guided Participant Recruitment

To enhance diagnostic objectivity in TCM trials, TRIPLE-TCM begins with an integrative identification of core TCM patterns for a target disease through cross-sectional surveys, Delphi expert consensus, and clinical data mining techniques such as machine learning–based clustering. Patient recruitment is then guided by a strict “Disease-Pattern Model”, requiring participants to share both the same biomedical diagnosis and a standardized TCM pattern.²⁴ This “One-disease–One-pattern” framework ensures a highly homogeneous sample while allowing for the presence of a single minor concomitant pattern per participant to preserve clinical representativeness. These secondary patterns are treated as stratification variables in subsequent statistical analyses.

Step 2: Hybrid Randomization with Preference Accommodation

A hybrid randomization scheme is adopted to preserve scientific rigor while accommodating patient preferences. Eligible participants are encouraged to undergo randomization into either an active TCM group (herbal or acupuncture) or a corresponding placebo control group. Those declining randomization due to strong treatment preferences are assigned directly to a non-randomized active TCM arm. This results in three parallel cohorts: randomized TCM, randomized placebo-TCM, and non-randomized TCM. Baseline imbalances between groups are addressed through both covariate adjustment and propensity-score matching.³⁵ All arms are analyzed under an intention-to-treat approach, with the non-randomized cohort providing additional insights into how patient preference may moderate treatment outcomes, including potential dose–response effects based on preference strength.

Step 3: Semi-Standardization in Intervention Delivery

Interventions are delivered using a semi-standardized protocol, combining a fixed core prescription targeting the primary TCM pattern with up to two individualized adjustments (herbs or acupoints) based on each participant’s minor pattern. In both randomized and non-randomized active TCM arms, clinicians are permitted to modify herbal formulas or acupuncture prescriptions within predefined limits. The placebo arms receive a standardized, non-modifiable sham intervention. Herbal treatments are prepared through commissioned decoction to ensure consistency, while matched placebo decoctions employ GMP-certified, sensorily indistinguishable preparation (ie, plant-flavored solution without any pharmacological activity). For acupuncture, two to three experienced practitioners—trained to adhere to a standardized protocol (eg, consistent needle placement, depth, and manipulation techniques in accordance with STRICTA guidelines³⁶)—administer the treatments to ensure consistency while accounting for practical constraints such as clinical duties and leave. Patient blinding is achieved using Streitberger placebo needles inserted at non-acupoint locations, positioned 0.5 Cun lateral to the real acupoints (see Figure 2).

Figure 2 Protocol for Delivering Semi-Standardized Interventions. The images have been adapted from Vecteezy under the Creative Commons CC-BY license. Abbreviation: CHM, Chinese Herbal Medicine.

Step 4: A Clinician-Patient Co-Assessment Model Incorporating TCM-Specific Outcomes and Validated Biomarkers

Outcome evaluation encompasses clinical efficacy, quality of life (QoL), and treatment adherence.

Efficacy is assessed using both standardized biomedical instruments and questionnaires (eg, polysomnography and PSQI for insomnia; FPG, PPG and HbA1C for diabetes) and a novel TCM-specific scale—the “TCM Therapeutic Effect Clinician-Patient Co-Evaluation Scale”, which was proposed by our team (see Table 3). This instrument, jointly completed by patients and clinicians, captures both subjective and objective dimensions of therapeutic response. Patients self-report their primary and secondary symptoms along with vital functional indicators central to TCM diagnostics (eg, appetite, pain, bowel and urinary habits, sleep, sweating, and, for women, menstruation and leukorrhea). Clinicians contribute structured assessments of clinical signs, tongue and pulse manifestations, overall condition, therapeutic response, and any adverse events observed. The integration of patient-reported components aligns with contemporary trends emphasizing PROs.³⁷ Given that many complementary and alternative therapies are designed to alleviate symptoms and enhance overall well-being, PROs are particularly well suited for evaluating their effectiveness.³⁸ While PROs are increasingly adopted in TCM trials for decision-making and policy guidance,³⁷ their standalone reliability for accurately capturing disease activity or severity remains debated.³⁹ To address this limitation, our co-evaluation scale integrates both PROs and clinician-assessed outcomes into a single instrument, enabling a more comprehensive and balanced appraisal of therapeutic effects.

Table 3 TCM Therapeutic Effect Clinician-Patient Co-Evaluation Scale

QoL assessment incorporates well-established instruments such as the MOS 36-Item Short-Form Health Survey (SF-36), EuroQol-5D (EQ-5D), and/or the WHO Quality of Life Questionnaire (WHOQOL), consistent with the patient-centered emphasis of pragmatic trial conventions, where changes in symptom and functional status constitute key outcomes.⁴⁰

Treatment adherence monitoring constitutes an essential third component. Despite initial randomization, protocol deviations (eg, nonadherence, cross-over, or dropout, etc.) may introduce confounding. As treatment effect estimates that ignore adherence patterns can misinform real-world therapeutic decisions,⁴¹ systematic tracking of compliance is mandated throughout the TCM trial period to ensure the interpretability of results.

Step 5: Cost-Utility Analysis Based on QALYs and Markov Decision-Analytic Model

In recent years, pharmacoeconomic studies have increasingly assessed the efficacy, safety, and affordability of Chinese herbal medicine to inform evidence-based decisions regarding essential medicine lists, national reimbursement policies, and drug price negotiations.⁴² However, acupuncture remains underrepresented, with limited studies yielding inconsistent conclusions regarding its cost-effectiveness.⁴³

To ensure policy relevance, TRIPLE-TCM incorporates comprehensive health-economic assessments. Quality-adjusted life years (QALYs) serve as the primary outcome measure to assess both clinical effectiveness and cost-effectiveness. For long-term projections, a Markov decision-analytic model will be constructed using trial-derived direct costs and QALYs to estimate incremental cost-effectiveness ratios. These data will inform clinical guideline development, optimize treatment strategies, and provide policymakers with robust evidence for resource allocation within the TCM system.^42–44

Key Insights and Conceptual Contributions

The evaluation of TCM interventions demands a nuanced approach that reconciles the inherent tension between its individualized clinical practice and the standardized methodologies of evidence-based research. Through a critical assessment of the pragmatic RCT framework proposed by Hu et al and comparative analysis with explanatory RCT principles, we demonstrate why the traditional “explanatory-pragmatic” dichotomy—when applied in isolation—fails to capture the complexity of TCM intervention evaluation. While explanatory RCTs provide rigorous efficacy data under controlled conditions, their artificial settings often compromise external validity, failing to reflect real-world TCM applications. Conversely, pragmatic RCTs, though more ecologically valid, struggle with confounding factors such as placebo effects and lack of blinding, particularly in therapies like acupuncture. We endorse Hu et al’s emphasis on incorporating patient preferences into trials and further argue that, in TCM research, such preferences should not be treated merely as confounders to eliminate, but rather strategically integrated and managed.

These insights constitute the central conceptual contribution of this commentary—introducing the TRIPLE-TCM framework, a trans-paradigm model integrating explanatory RCTs, pragmatic RCTs, and PRPP to simultaneously assess efficacy and effectiveness while accommodating TCM’s personalized diagnostics. TRIPLE-TCM embodies not only a theoretical advance but also a systematic operational scheme composed of multiple interrelated modules, including pattern-guided recruitment, preference-embedded randomization, semi-standardized interventions, and clinician-patient co-assessment.

This hybrid framework balances scientific rigor with clinical relevance across multiple levels:

• At the “Diagnostic” level, the “disease-pattern model” standardizes TCM diagnoses across participants, establishing a homogeneous basis for comparison.
• At the “Treatment” level, the semi-standardized protocol of “core formula + individualized modifications” accommodates both consistency in the intervention and necessary clinical flexibility within the same trial.
• At the “Assessment” level, the “clinician-patient co-assessment model” integrates biomedical indicators, PROs, and TCM-specific measures, offering a multidimensional and standardized tool for evaluating the effects and safety of complex TCM interventions.

Summary and Research Outlook

This study introduces the TRIPLE-TCM framework, a novel trial design that ensures the internal validity of therapeutic effects through randomized cohorts; it also analyzes, through non-randomized preference cohorts, the potential moderating effect of patient preference on treatment outcomes, thus enhancing both external validity and recruitment feasibility. Nonetheless, the framework remains in its early conceptual stage.

For future work, we propose the following steps:

First, the “TCM Therapeutic Effect Clinician-Patient Co-Evaluation Scale” embedded in the TRIPLE-TCM framework should undergo rigorous psychometric validation to ensure reliability and validity, providing a robust tool for subsequent empirical studies.

Second, once the scale demonstrates satisfactory psychometric properties, pilot feasibility studies should be initiated to evaluate the framework’s real-world implementation. Initial trials may focus on diseases with well-defined TCM syndrome patterns and established therapeutic advantages. For instance, our team’s prior meta-analyses and clinical trials on acupuncture and CCPP for conditions like primary insomnia^45,46 and depressive disorders⁴⁷ suggest significant therapeutic potential, albeit the need for improved evidence quality. These conditions are therefore well-suited for validating the TRIPLE-TCM framework.

Finally, the framework’s applicability in cross-cultural contexts, especially in non-Asian regions, must be assessed. These settings may present specific challenges, including limited TCM awareness, a scarcity of qualified practitioners, low cultural acceptance of diagnostic methods such as tongue and pulse examination, and difficulties in designing and implementing sensory-matched placebo controls.

Through these systematic efforts, we hope TCM research can better align with global evidence standards while preserving its holistic principles, ultimately enhancing its credibility and integration into mainstream healthcare.

Abbreviations

CCPP(s), Commercial Chinese Polyherbal Preparation(s); EQ-5D, EuroQol-5D; FPG, Fasting Plasma Glucose; GMP, Good Manufacturing Practice; HbA1C, Hemoglobin A1C; PPG, Postprandial Plasma Glucose; PPT(s), Patient-Preference Trial(s); PSQI, Pittsburgh Sleep Quality Index; PROs, Patient-Reported Outcomes; PRPP, Partially Randomised Patient Preference; QoL, Quality of Life; QALYs, Quality-Adjusted Life Years; RCT(s), Randomized Controlled Trial(s); SF-36, The MOS 36-Item Short-Form Health Survey; STRICTA, STandards for Reporting Interventions in Clinical Trials of Acupuncture; SUI, Stress Urinary Incontinence; TCM, Traditional Chinese Medicine; TRIPLE-TCM, Trans-paradigm Randomized-Individualized-Preference-Linked Efficacy/Effectiveness Evaluation for TCM; WHOQOL, The WHO Quality of Life Questionnaire.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agreed to be accountable for all aspects of the work.

Funding

This work was supported by the Scientific Research Fund Project of Shanghai Sanda University [2024BSZX03] to FY-Z.

Disclosure

The authors declare no competing interests in this work.

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45. Zhao FY, Fu QQ, Kennedy GA, et al. Can acupuncture improve objective sleep indices in patients with primary insomnia? A systematic review and meta-analysis. Sleep Med. 2021;80:244–259. doi:10.1016/j.sleep.2021.01.053

46. Zhao FY, Xu P, Kennedy GA, et al. Commercial Chinese polyherbal preparation Zao Ren An Shen prescription for primary insomnia: a systematic review with meta-analysis and trial sequential analysis. Front Pharmacol. 2024;15:1376637. doi:10.3389/fphar.2024.1376637

47. Zhao FY, Zheng Z, Fu QQ, et al. Acupuncture for comorbid depression and insomnia in perimenopause: a feasibility patient-assessor-blinded, randomized, and sham-controlled clinical trial. Front Public Health. 2023;11:1120567. doi:10.3389/fpubh.2023.1120567

Donald Trump has said that he doesn’t know who Changpeng Zhao is despite pardoning the billionaire founder of cryptocurrency exchange Binance in October.

The US president was asked in a 60 Minutes interview that aired on Sunday why he pardoned Zhao, who is also known as “CZ”, for enabling money laundering despite him causing “significant harm to … national security” according to federal prosecutors.

“OK, are you ready? I don’t know who he is,” Trump told Norah O’Donnell, the host of CBS News’ 60 Minutes. Trump added that he did not remember meeting Zhao, had “no idea who he is” other than being told that the multibillionaire crypto boss was a victim of a “witch-hunt” by former president Joe Biden.

In 2023, Zhao pleaded guilty to charges that he broke rules designed to stop money laundering – after Binance allegedly failed to report suspicious transactions with organizations including Hamas and al-Qaida.

Zhao apologized, paid a $50m fine and had served nearly four months in prison before being pardoned by Trump, with the White House saying he was prosecuted due to Biden’s “war on cryptocurrency”.

Trump has said he wants the US to be a leader in cryptocurrency and Zhao, in thanking the president for his pardon, promised on X to “do everything we can to help make America the Capital of Crypto”.

Zhao has kept his stake in Binance, the world’s largest crypto exchange, which has had business dealings with World Liberty Financial, a cryptocurrency company owned by Trump’s family.

Trump’s failure to recall his pardon for Zhao comes amid an extensive Republican pursuit of the prior Biden administration for allegedly covering up the former president’s mental and physical decline as well as for his autopen signatures – which come from a machine that replicates a person’s signature and allows someone to more easily sign large quantities of documents.

On 28 October, the Republican-led House oversight committee released a report centering on Biden and autopen signatures. The Republican committee chair, James Comer of Kentucky, furthermore maintained that the use of autopen was “one of the biggest political scandals in US history”. Some Republicans want pardons signed by autopen under Biden to be voided.

It is unclear whether Trump’s pardon for Zhao was signed by autopen. Some of those pardoned by Trump for their role in the 6 January 2021 attack on the US Capitol after the lost the 2020 White House election to Biden have also claimed that Trump did not sign their pardons at all.

Taste of Italy 2, a private equity fund managed by DeA Capital Alternative Funds SGR specializing in the agri-food sector, has acquired from the Europe Capital Partners VII fund a majority stake in Fine Food Group, the leading Italian distributor of premium Tex-Mex, American and fusion foodservice products. The transaction included the reinvestment of Fine Food’s founder and CEO, Fabrizio Fasulo, who will continue to lead the company.

DeA Capital was advised by Baker McKenzie with a team led by Partner Paolo Ghiglione, assisted by Counsel Chiara Marinozzi and Associate Giacomo Lamperti; by Partner Carlo de Vito Piscicelli and Senior Associate Edoardo Filiberto Roversi for the banking aspects; by Partners Francesco Pisciotta and Davide Chiesa for the tax aspects; and by Senior Counsel Alessia Raimondo for the labour aspects.

Pavia e Ansaldo advised DeA Capital for the antitrust matters.
Gatti Pavesi Bianchi Ludovici advised Europe Capital Partners VII and the other sellers.

Simmons & Simmons advised Mr. Fabrizio Fasulo, meanwhile Ashurst advised the financing Banks.

Fiserv Small Business Index remains at 148

Year-over-year sales grew +1.5%

MILWAUKEE–(BUSINESS WIRE)–
Fiserv, Inc. (NYSE: FI), a leading global provider of payments and financial services technology, has published the Fiserv Small Business Index for October 2025, with the seasonally adjusted Index holding steady at 148.

Year-over-year sales (+1.5%) and transactions (+1.1%) both grew, but this was the slowest annual sales growth rate since February 2025. Month-over-month sales (+0.1%) and transactions (+0.1%) each saw small increases compared to September 2025. When adjusted for inflation, small business sales (-1.4%) declined year over year, the steepest decline of the previous eight months.

“Consumers continued spending cautiously in October, pulling back significantly at restaurants and across many discretionary categories,” said Prasanna Dhore, Chief Data Officer, Fiserv. “Small retailers, however, did see an uptick in sales – a positive indicator as these businesses move deeper into the holiday shopping season.”

Key Takeaways

Retail Accelerates from September, Year-Over-Year Pace Slows

Small business retail sales grew (+0.7%) month over month and (+0.6%) year over year. Core Retail, which excludes more volatile categories, was stronger, with sales growth (+1.6%) month over month and (+2.6%) year over year. Significant monthly sales gains were in Sporting Goods (+3.0%) and Clothing (+1.9%), two discretionary categories that benefit from early holiday-themed sales activity. Food and Beverage (grocery) sales were up (+1.4%) month over month, buoyed by increased foot traffic and higher average tickets.

Discretionary Spending Stalls, Essential Purchases Deliver Most of the Growth

Discretionary spending rose (+0.2%) year over year, but Essential sales growth maintained a faster pace (+2.5%), widening the gap between Essentials and Discretionary. Compared to September, Discretionary spending was unchanged (+0.0%) while Essentials rose modestly (+0.4%).

Restaurant Sales Slip Across the Board

Restaurants continue to face headwinds, with sales growing slightly (+0.1%) year over year for October, and declining (-0.3%) month over month. Bars and pubs saw a decrease in foot traffic (-0.5%) and sales (-0.1%) compared to September. Full-service restaurants continued to struggle with month-over-month sales (-0.1%) and foot traffic (-0.2%) dropping while average tickets (+0.2%) grew. Limited service (or quick-service) restaurants saw month-over-month sales (-0.6%) and foot traffic (-0.8%) decrease, while average tickets rose (+0.2%).

To access the full Fiserv Small Business Index, visit fiserv.com/FiservSmallBusinessIndex.

About the Fiserv Small Business Index^®

The Fiserv Small Business Index is published during the first week of every month and differentiated by its direct aggregation of consumer spending activity within the U.S. small business ecosystem. Rather than relying on survey or sentiment data, the Fiserv Small Business Index is derived from point-of-sale transaction data, including card, cash, and check transactions in-store and online across approximately 2 million U.S. small businesses, including hundreds of thousands leveraging the Clover point-of-sale and business management platform.

Benchmarked to 2019, the Fiserv Small Business Index provides a numeric value measuring consumer spending, with an accompanying transaction index measuring customer traffic. Through a simple interface, users can access data by region, state, and/or across business types categorized by the North American Industry Classification System (NAICS) Level-6 Classification System. The Fiserv Small Business Index provides visibility into more than 70 industries, allowing users to track sales trends with precision and understand the diverse dynamics shaping the U.S. small business economy.

About Fiserv

Fiserv, Inc. (NYSE: FI), a Fortune 500 company, moves more than money. As a global leader in payments and financial technology, the company helps clients achieve best-in-class results through a commitment to innovation and excellence in areas including account processing and digital banking solutions; card issuer processing and network services; payments; e-commerce; merchant acquiring and processing; and Clover^®, the world’s smartest point-of-sale system and business management platform. Fiserv is a member of the S&P 500^® Index, one of TIME Magazine’s Most Influential Companies™ and one of Fortune^® World’s Most Admired Companies™. Visit fiserv.com and follow on social media for more information and the latest company news.

FI-G

View source version on businesswire.com: https://www.businesswire.com/news/home/20251103731873/en/

For more information contact:

Media Relations:

Chase Wallace

Director, Communications

+1 470-481-2555

chase.wallace@fiserv.com

Source: Fiserv, Inc.

Released November 3, 2025

The head of the US Food and Drug Administration’s drug center abruptly resigned on Sunday after federal officials began reviewing “serious concerns about his personal conduct”, according to a government spokesperson.

Dr George Tidmarsh, who was named to the FDA post in July, was placed on leave on Friday after officials in the Department of Health and Human Services’ (HHS) office of general counsel were notified of the issues, the HHS press secretary, Emily Hilliard, said in an email. Tidmarsh then resigned on Sunday morning.

Hilliard said the HHS secretary, Robert F Kennedy Jr, “expects the highest ethical standards from all individuals serving under his leadership and remains committed to full transparency”.

The departure came the same day that a drugmaker connected to one of Tidmarsh’s former business associates filed a lawsuit alleging that he made “false and defamatory statements” during his time at the FDA.

The lawsuit, brought by Aurinia Pharmaceuticals, alleges that Tidmarsh used his FDA position to pursue a “longstanding personal vendetta” against the chair of the company’s board of directors, Kevin Tang.

Tang previously served as a board member of several drugmakers where Tidmarsh was an executive, including La Jolla Pharmaceutical, and was involved in his ouster from those leadership positions, according to the lawsuit.

Messages placed to Tidmarsh and his lawyer were not immediately returned late on Sunday.

Tidmarsh founded and led a series of pharmaceutical companies over several decades working in California’s pharmaceutical and biotech industries. Before joining the FDA, he also served as an adjunct professor at Stanford University. He was recruited to join the agency over the summer after meeting with the FDA commissioner, Marty Makary.

Tidmarsh’s ouster is the latest in a string of haphazard leadership changes at the agency, which has been rocked for months by firings, departures and controversial decisions on vaccines, fluoride and other products.

Dr Vinay Prasad, who oversees FDA’s vaccine and biologics center, resigned in July after coming under fire from conservative activists close to Donald Trump, only to rejoin the agency two weeks later at the behest of Kennedy.

The FDA’s drug center, which Tidmarsh oversaw, has lost more than 1,000 staffers over the past year to layoffs or resignations, according to agency figures. The center is the largest division of the FDA and is responsible for the review, safety and quality control of prescription and over-the-counter medicines.

In September, Tidmarsh drew public attention for a highly unusual post on LinkedIn stating that one of Aurinia Pharmaceutical’s products, a kidney drug, had “not been shown to provide a direct clinical benefit for patients”. It is very unusual for an FDA regulator to single out individual companies and products in public comments online.

According to the company’s lawsuit, Aurinia’s stock dropped 20% shortly after the post, wiping out more than $350m in shareholder value.

Tidmarsh later deleted the LinkedIn post and said he had posted it in his personal capacity – not as an FDA official.

Aurinia’s lawsuit also alleges, among other things, that Tidmarsh used his post at the FDA to target a type of thyroid drug made by another company, American Laboratories, where Tang also serves as board chair.

The lawsuit, filed in Maryland’s US district court, seeks compensatory and punitive damages and “to set the record straight”, according to the company.

Zscaler, Inc. (NASDAQ: ZS), the leader in cloud security, today announced it has acquired innovative AI security pioneer SPLX, extending the Zscaler Zero Trust Exchange^TM platform with shift-left AI asset discovery, automated red teaming, and governance, so organizations can secure their AI investments from development through deployment. 

“Today marks an important step in advancing Zscaler’s role as the trusted partner helping organizations securely adopt AI,” said Jay Chaudhry, CEO, Chairman, and Founder of Zscaler. “AI is creating enormous value, but its full potential can only be realized when it can be secured. By combining SPLX’s technology with the intelligence of the Zscaler Zero Trust Exchange and its native data protection that classifies, governs, and prevents loss of sensitive data across prompts, models, and outputs, Zscaler will secure the entire AI lifecycle on one platform. This will strengthen our industry leadership and give customers the confidence to safely embrace AI.”

As AI drives record infrastructure investments projected to exceed $250 billion by end of 2025¹, organizations face a rapidly expanding attack surface and shadow AI sprawl. Continuously evolving models, agents, and large language models (LLMs) require ongoing discovery, risk assessment, and remediation, while AI agents and Model Context Protocol (MCP) servers demand strict guardrails and new techniques to secure data and AI assets across the lifecycle.

SPLX’s innovative technology and deep expertise in AI red teaming, asset management, threat inspection, prompt hardening and governance will expand Zscaler’s current capabilities, creating a new, dedicated and natively integrated layer of AI protection within the Zscaler Zero Trust Exchange platform, that includes: 

• AI Asset Discovery and Risk Assessment: Discovery extends beyond public generative AI applications and public clouds to include AI models, workflows, code repositories and RAGs and MCP servers in both public and private deployments.
   
• Automated AI Red Teaming and Remediation: From development to production, with 5,000+ purpose-built and domain specific attack simulations to find risks and vulnerabilities, and offer remediation in real time. 
   
• AI Runtime Guardrails and Prompt Hardening: Expands Zscaler’s current AI Runtime Guardrails that protect sensitive data and block malicious attacks between AI apps and LLMs, as well as agentic workflows, to include deep visibility within development environments and automate Guardrails for risky AI assets.
   
• AI Governance and Compliance: Risk mitigation and support for organizations to shift from reactive defense to proactive protection for their valuable AI investments, and comply with governance frameworks. 

“Zscaler and SPLX share a vision to confront the vast new attack surface created by rapidly expanding AI infrastructure investments,” said Kristian Kamber, CEO and co-founder of SPLX. “By joining forces, we’ll bring our innovation to one of the most trusted security platforms in the world, securing AI innovation at the speed organizations are adopting it.” 

Source: 1) Goldman Sachs, “Technology in 2025: The Cycle Rolls On” February 2, 2025

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Forward-Looking Statements

This press release contains forward-looking statements that are based on our management’s beliefs and assumptions and on information currently available to our management. These forward-looking statements include the expected benefits of the proposed acquisition to Zscaler and its customers and plans regarding SPLX’s capabilities. These forward-looking statements are subject to the safe harbor provisions created by the Private Securities Litigation Reform Act of 1995. A significant number of factors could cause actual results to differ materially from statements made in this press release, including those factors related to Zscaler’s ability to successfully integrate SPLX technology into our cloud platform, the potential impact of the acquisition to the existing SPLX business and the retention of SPLX employees. Additional risks and uncertainties are set forth in our most recent Annual Report on Form 10-K filed with the Securities and Exchange Commission (“SEC”) on September 11, 2025, which is available on our website at ir.zscaler.com and on the SEC’s website at www.sec.gov. Any forward-looking statements in this release are based on the limited information currently available to Zscaler as of the date hereof, which is subject to change, and Zscaler will not necessarily update the information, even if new information becomes available in the future.

Expanded collaboration makes Falcon Next-Gen SIEM the foundation of EY global cyber managed services for security and non-security data, accelerating AI-driven security transformation for clients

AUSTIN, Texas – November 3, 2025 – CrowdStrike (NASDAQ: CRWD) and Ernst & Young LLP (EY US) today announced that EY US has selected CrowdStrike Falcon® Next-Gen SIEM as the foundational platform powering its global cybersecurity managed services. With Falcon Next-Gen SIEM and EY US managed services experience, enterprises worldwide can accelerate the move beyond legacy security information and event management (SIEM) and modernize security operations at scale.

Adversaries are moving at the speed of AI, scaling attacks faster than defenders can respond. Legacy SIEM, built for a different era, is too slow, noisy, and costly to stop today’s threats. Falcon Next-Gen SIEM delivers real-time speed, efficiency, and outcomes legacy platforms can’t match – and will be further strengthened by CrowdStrike’s acquisition of Onum, a real-time data pipeline platform. 

By standardizing its global managed services on Falcon Next-Gen SIEM for security and non-security data, EY will equip clients with AI-powered protection that moves faster and sees more, enabling organizations to replace outdated SIEM with a modern platform that delivers measurable outcomes at scale.

“The agentic era is accelerating everything, and legacy SIEMs simply can’t cope with threat landscape realities as well as enterprise data proliferation,” said Daniel Bernard, chief business officer at CrowdStrike. “By making Falcon Next-Gen SIEM the foundation of EY US global managed services, we’re helping clients modernize faster and achieve outcomes legacy tools could never deliver.”

Key improvements for clients include:

• Accelerated Migration: EY US professionals will help enterprises move from legacy SIEM to Falcon Next-Gen SIEM, achieving substantial efficiencies and up to 150% faster search.
• AI-Powered, Adversary-Driven Protection: Falcon Next-Gen SIEM unifies CrowdStrike first- and third-party platform data with real-time threat intelligence and AI-powered automation, delivering enterprise-wide visibility and supercharging detection and response.
• Next-Generation Operating Model: EY Managed Services helps clients turn data into a competitive edge by reducing operational burden, increasing cost certainty, accelerating AI adoption, and building smarter operating models that lower risk and unlock strategic value.
• Global Security Operations Center (SOC): The experience of EY US in SOC helps clients strengthen cyber and operational resilience by reducing attack surface exposure, securing digital identity, and managing cybersecurity risks with 24/7 advanced defense across 160 countries.
• Unified Visibility and Scale: CrowdStrike consolidates data into a single platform, delivering complete visibility and massive scale without the cost and complexity of legacy solutions.

“Our clients need security that’s faster, simpler and more effective,” said Tapan Shah, EY Global and Americas Cybersecurity Managed Services Leader. “With EY US as the first mover in building our cyber managed services on Falcon Next-Gen SIEM, we see this as more than a technology upgrade – it’s a strategic move to embrace AI security operations. EY US teams bring deep sector and AI experience, delivering high-impact cybersecurity outcomes that improve operations and efficiency across the enterprise.” 

About EY

EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.

Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.

EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multidisciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.

All in to shape the future with confidence.

EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients. Information about how EY collects and uses personal data and a description of the rights individuals have under data protection legislation are available via ey.com/privacy. EY member firms do not practice law where prohibited by local laws. For more information about our organization, please visit ey.com.

Ernst & Young LLP is a client-serving member firm of Ernst & Young Global Limited operating in the US.

About CrowdStrike

CrowdStrike (NASDAQ: CRWD), a global cybersecurity leader, has redefined modern security with the world’s most advanced cloud-native platform for protecting critical areas of enterprise risk – endpoints and cloud workloads, identity and data.

Powered by the CrowdStrike Security Cloud and world-class AI, the CrowdStrike Falcon® platform leverages real-time indicators of attack, threat intelligence, evolving adversary tradecraft and enriched telemetry from across the enterprise to deliver hyper-accurate detections, automated protection and remediation, elite threat hunting and prioritized observability of vulnerabilities.

Purpose-built in the cloud with a single lightweight-agent architecture, the Falcon platform delivers rapid and scalable deployment, superior protection and performance, reduced complexity and immediate time-to-value.

CrowdStrike: We stop breaches.

Learn more: https://www.crowdstrike.com/

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Start a free trial today: https://www.crowdstrike.com/trial

© 2025 CrowdStrike, Inc. All rights reserved. CrowdStrike and CrowdStrike Falcon are marks owned by CrowdStrike, Inc. and are registered in the United States and other countries. CrowdStrike owns other trademarks and service marks and may use the brands of third parties to identify their products and services.

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Jake Schuster

CrowdStrike Corporate Communications

press@crowdstrike.com

 

Cannabinoid receptor 1 (CB₁), which is majorly expressed in the central nervous system (CNS) belongs to the class A G-protein coupled receptor (GPCR) family proteins (Hua et al., 2016; Mackie, 2008; Zou and Kumar, 2018; Dutta and Shukla, 2023). GPCRs are expressed in the cellular membrane and help transduce chemical signals from the extracellular to the intracellular direction with the help of the downstream signaling proteins (G-proteins and β-arrestin) (Rosenbaum et al., 2009; Latorraca et al., 2017; Weis and Kobilka, 2018). In addition, GPCRs are the largest family of drug targets due to their substantial involvement in human pathophysiology and druggability (Hauser et al., 2017; Yang et al., 2021). Significant research efforts have been invested in the discovery of drugs targeting CB₁, which helps to maintain homeostasis in neuron signaling and physiological processes (Smith et al., 2010; An et al., 2020).

Initial drug discovery efforts, especially the design of synthetic agonists, were based on modifying the scaffolds of phytocannabinoids (e.g. ${\mathrm{\Delta }}^9$-Tetrahydrocannabinol, cannabinol) and endocannabinoids (e.g. Anandamide, 2-arachidonoylglycerol) (Figure 1; Pertwee, 2006; Pertwee and Ross, 2002; Pertwee et al., 2010). The synthetic molecules, which maintain the aromatic, pyran, and cyclohexenyl ring of the most common psychoactive phytocannabinoid ${\mathrm{\Delta }}^9$-THC, are known as classical cannabinoids (Figure 1—figure supplement 1; Razdan, 2009 Madras, 2018; Dutta et al., 2022a). However, the pharmacological potential of these molecules was diminished due to their psychological and physiological side effects (‘tetrad’ side effect) (Moore and Weerts, 2022; Wang et al., 2020; Tummino et al., 2023). One such example of a synthetic cannabinoid is 1,1-Dimethylheptyl-11-hydroxy-tetrahydrocannabinol (commonly known as HU-210), which is a Schedule I controlled substance in the United States (Farinha-Ferreira et al., 2022).

Apart from the canonical structures of synthetic cannabinoids, molecules with diverse scaffolds were also synthesized through structure-activity studies (Wiley et al., 2016; Schoeder et al., 2018; Walsh and Andersen, 2020). However, these molecules also lacked any pharmacological importance due to psychological side effects (Akram et al., 2019; Worob and Wenthur, 2020). Due to the diverse structures and psychological effects, these molecules became unregulated substitutes for traditional illicit substances (Peacock et al., 2019). These synthetic cannabinoids belong to a class of molecules known as NPS as these molecules are not scheduled under the Single Convention on Narcotic Drugs (1961) or the Convention on Psychotropic Substances (1971) (Peacock et al., 2019; Madras, 2016). Synthetic cannabinoids make up the largest category of NPS molecules (Shafi et al., 2020; Alam and Keating, 2020). NPS creates a significant challenge for drug enforcement agencies, as they appeal to drug users seeking ‘legal highs’ to avoid the legal consequences of using traditional drugs and to be undetectable in drug screenings (Worob and Wenthur, 2020).

The molecular structures of NPS synthetic cannabinoids consist of four pharmacophore components: linked, linker, core, and tail groups (Worob and Wenthur, 2020; Potts et al., 2020). The core usually consists of aromatic scaffolds (e.g. indole, indazole, carbazole, benzimidazole) (Figure 1—figure supplement 2; Schoeder et al., 2018). The tail and linker groups are connected to the core. In the tail group, long alkyl chain-like scaffolds are ubiquitous in most NPSs; however, molecules with bulkier cyclic chains (e.g. AB-CHMINACA) are also present (Potts et al., 2020). Frequently encountered scaffolds in linker groups are methanone, ethanone, carboxamide, and carboxylate ester groups (Hill et al., 2018). The linker acts as a bridge between the core and the linked group. In the initial NPS synthetic cannabinoids, the linked group included polyaromatic rings; however, non-cyclic linked groups have also been identified in NPS recently (Schoeder et al., 2018; Potts et al., 2020). Structural diversity in every component, while maintaining high binding affinity and potency for CB₁ make these molecules easier for drug manufacturers and harder to ban by drug enforcement agencies (Banister et al., 2015a; Ametovski et al., 2020; Cannaert et al., 2020; Banister et al., 2015b).

The use of NPS synthetic cannabinoids has been found to cause more physiological side effects than traditional cannabinergic ‘tetrad’ side effects (Tai and Fantegrossi, 2014). These side effects include tachycardia, drowsiness, dizziness, hypertension, seizures, convulsions, nausea, high blood pressure, and chest pain (Tai and Fantegrossi, 2014; Finlay et al., 2019). For instance, Gatch and Forster have shown that the high concentrations of AMB-FUBINACA, the molecule which caused ‘zombie outbreak’ in New York, induced tremors (Gatch and Forster, 2019; Adams et al., 2017). A recent biochemical study has linked these discriminatory effects with the differential signaling of β-arrestin (Finlay et al., 2019). According to Finlay et al., NPS shows higher β-arrestin signaling compared to the classical cannabinoids, which has also been confirmed by other β-arrestin signaling studies (Finlay et al., 2019; Grafinger et al., 2021). However, a mechanistic understanding of these differential downstream signaling effects between NPS and classical cannabinoids is still missing.

Mutagenesis studies have shown that the conserved NPxxY motif of CB₁ have a larger role in downstream β-arrestin signaling than G-protein signaling (Leo et al., 2023; Liao et al., 2023). Recently published MDMB-FUBINACA bound CB₁-β-arrestin-1 complex structure also points out the importance of the unique triad interaction (Y397^7.53-Y294^5.58-T210^3.46) involving NPxxY motif in β-arrestin-1 signaling (Liao et al., 2023). However, structural comparison of the classical cannabinoid (AM841) and NPS (MDMB-FUBINACA) bound active CB₁-G_i complex shows a conformationally similar NPxxY motif (Figure 2; Krishna Kumar et al., 2019; Hua et al., 2020). In light of these experimental observations, it can be inferred that higher β-arrestin signaling stems from higher dynamic propensity of triad interaction formation for NPS-bound CB₁. We hypothesized that distinct orthosteric pocket interactions for NPS and classical cannabinoids cause differential allosteric modulation of intracellular dynamics that facilitate triad interaction.

To study these distinct dynamic effects, we compared the (un)binding of the classical cannabinoid (HU-210) and NPS (MDMB-FUBINACA) from the receptor binding site. These molecules have nanomolar affinities. Obtaining the initial pathway of ligand unbinding from unbiased sampling will be computationally expensive. Therefore, a well-tempered metadynamics approach was used to sample the unbinding event, where a time-dependent biased potential is deposited for the faster sampling of the metastable minima along the pathway (Barducci et al., 2008). However, a detailed characterization of the unbinding processes is only possible through the thermodynamics and kinetics estimation of intermediate states. Thus, a transition operator-based approach is needed, which helps to estimate the transition timescale between the states and the stationary density of each state. Estimation from these approaches usually depends on the equilibrium between the local states, which can only be maintained by reversible sampling. For high-affinity ligands like MDMB-FUBINACA and HU-210, reversible sampling is expensive as ligands move from high energy unbound states to lower energy bound states irreversibly. Hence, we implemented a transition operator approach named the transition-based reweighting analysis (TRAM) method, which can tackle this lack of local equilibrium between states by combining unbiased and biased approaches (Wu et al., 2016). TRAM has been used in in different simulation studies for estimating thermodynamics and kinetics of processes that have high free energy barriers. For example, TRAM have been utilized for characterization of small molecule and peptide (un)binding processes (Wu et al., 2016; Paul et al., 2017; Ge et al., 2021; Spiriti et al., 2022; Ge and Voelz, 2022), protein dimerization (Meral et al., 2018), ion transportation (Hu et al., 2019). To implement TRAM for our study, extensive sampling of the (un)binding process of both ligands was performed using a combination of umbrella sampling and unbiased simulations from the pathway obtained using metadynamics (see Methods section) (Kästner, 2011). We showed that TRAM can produce consistent kinetic estimation with less unbiased simulation data compared to traditional methods like the Markov state model (Prinz et al., 2011).

Based on estimates of thermodynamics and kinetics, it was observed that both NPS and classical cannabinoids have similar unbinding pathways. However, their unbinding mechanisms differ due to the aromatic tail of the MDMB-FUBINACA compared to the alkyl side chain of HU-210. Furthermore, dynamic interaction calculations reveal a major difference with TM7 between NPS and classical cannabinoid. Specifically, the hydroxyl group in the benzopyran moiety of HU-210 forms much stronger polar interactions with S383^7.39 compared to the carbonyl oxygen of the linker group in MDMB-FUBINACA. MD simulations of other classical cannabinoids and NPS molecules bound to CB₁ also support these significant interaction differences. The ligand binding effect in intracellular signaling was estimated by measuring the probability of triad formation in the intracellular region. NPS-bound CB₁ shows higher probability of forming triad interaction compared to the classical cannabinoids, which supports the experimental observations of high $\beta$-arrestin signaling of NPS-bound receptors. To validate that the triad formation is indeed caused by the binding pocket interaction differences between the two ligands, allosteric strength binding pocket residues and NPxxY motif was estimated with the deep learning technique, Neural relational inference (NRI) (Zhu et al., 2022a). NRI network revealed that binding pocket residues of NPS-bound ensemble have higher allosteric weights for the NPxxY motif compared to classical cannabinoids. These analyses validate our hypothesis that the differential dynamic allosteric control of the NPxxY motif might lead to the $\beta$-arrestin signaling for different ligands. This study provides a foundation for additional computational and experimental research to enhance our understanding of the connection between ligand scaffolds and downstream signaling. This knowledge will assist drug enforcement agencies in proactively banning these molecules and inform policies that can protect individuals from the effects of abuse.