Non-small cell lung cancer is one of the most common malignancies with major challenges in diagnosis and treatment. Estimating individual prognosis is important in order to tailor the selected therapy to each patient. Lymph node involvement represents a crucial factor which is associated with long-term survival. The currently used classifications of lymph node involvement are anatomically conditioned and do not consider the number of positive lymph nodes and their association with non-involved lymph nodes. In this study we investigated the effect of a mathematical formula (log-odds ratio), including the ratio of positive and negative lymph nodes on survival. We found that this new score is significantly associated with survival in resectable non-small cell lung cancer and might better reflect the individual prognosis.p < 0.001, chi-squared test 41.26). When combined with N1 and N2 descriptors, LN-LOR low risk (median OS not reached and 83 months) and LN-LOR high-risk patients (median OS 50 and 59 months) had similar survival irrespective of the anatomical location of the positive lymph nodes. Multivariable Cox regression analysis revealed that age , sex , histological subtype , pathological stage  and LN-LOR risk groups  were independent prognostic factors for OS. Conclusions: This retrospective two-center analysis shows that LN-LOR is significantly associated with OS in resectable NSCLC and might better reflect the biological behavior of the disease, regardless of anatomical lymph node locations. This finding may additionally support the value of extensive LN dissection.Objectives: The ratio of positive and resected lymph nodes (LN ratio) has been shown to be prognostic in non-small cell lung cancer (NSCLC). Contrary to the LN ratio, calculating the LN log-odds ratio (LN-LOR) additionally considers the total number of resected lymph nodes. We aim to evaluate LN-LOR between positive and resected lymph nodes as a prognostic factor in operable NSCLC. Methods: Patients with NSCLC who underwent curative intent lobectomy treated at two high-volume centers were retrospectively studied. LN-LOR was dichotomized according to impact on OS and further combined with N descriptors and correlated with clinical variables and survival. Results: 944 patients were included. Cut-off analysis revealed that an LN-LOR of −0.34 significantly discriminated patients according to OS ( Lymph node (LN) involvement is a well established and strong prognostic factor in resectable non-small cell lung cancer (NSCLC) and represents an integral part of the current TNM staging system ,2. The mRetrospective data collection was approved based on the ethical standards prescribed by the Helsinki Declaration of the World Medical Association and with the permission of the Scientific and Research Committee of the City of Vienna (EK22-096-VK) and the Medical University of Vienna (EK1662/2016), which waived the need for individual informed consent for the retrospective data collection.This is a retrospective non-interventional cohort study reviewing prospective databases, including all consecutive patients with complete data who underwent curative intent lobectomy with lymphadenectomy for stage I-III NSCLC with follow-up at two specialized institutions in Vienna, Austria, between 2008 and 2017. Only 24 patients with missing relevant information during the study period have been excluded. In order to investigate a homogenous study population, patients undergoing bilobectomy, pneumonectomy or sublobar resection and patients without R0 resection have been excluded. Only seventeen (1.8%) patients had no standardized mediastinal lymphadenectomy according to ESTS guidelines and were kept in the analysis in order to evaluate the impact of lymphadenectomy and LN-LOR in N0 patients. Predefined data were reviewed retrospectively, focusing on resected and pathologically assessed LNs and long-term survival . After pt-test. Kaplan–Meier survival curves and two-sided log-rank tests were used for univariable survival analyses. The median follow-up time was estimated using the reverse-censored Kaplan–Meier method. The Cox proportional hazards model was used for univariable survival analyses to calculate the hazard ratios (HR) and corresponding 95% CI. A multivariable Cox regression model was used to evaluate the influence of LN-LOR (dichotomous) on OS and to calculate the hazard ratios (HR) and corresponding 95% CI. The multivariable model was adjusted for sex (dichotomous), age (continuous), histological subtype , pathological T factor , pathological N factor , overall stage , neoadjuvant treatment (dichotomous) and adjuvant treatment (dichotomous). The p values are always given as two-sided and were considered statistically significant below 0.05. Metric data is always shown as median or mean and corresponding interquartile range (IQR) or, in case of OS, as median and corresponding 95% CI. Unless otherwise stated, statistical calculations were performed using SPSS Statistics version 28  and GraphPad Prism version 9 .LN-LOR was calculated using the formula ‘log [(positive LNs + 0.5)/(resected LNs—positive LNs + 0.5)]’ in which 0.5 was added to both the numerator and denominator to avoid singularity . After cAll methods and results are reported according to the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) and “Statistical and data reporting guidelines for the European Journal of Cardio-Thoracic Surgery and the Interactive CardioVascular and Thoracic Surgery” guidelines ,19.Inclusion criteria resulted in a study size of 944 patients . In total, 529 males (56%) and 415 females (44%) with a mean age of 65 years  were included. Adenocarcinoma  was the most common histological subtype. Most patients had pT1/pT2 tumors (90%) and were node negative (76%). Accordingly, 63% of patients in this cohort were in pathological stage I. The median number of lymph nodes resected during lymphadenectomy was 12 (IQR 11), and 74 (8%) and 190 (20%) patients underwent neoadjuvant or adjuvant treatment, respectively. Basic patient characteristics are summarized in p < 0.001, chi-squared 41.26). According to OS, we defined an LN-LOR low risk  and an LN-LOR high risk  group. Notably, LN-LOR high-risk patients had a statistically significant and clinically relevant worse OS compared to low-risk patients   .p = 0.001) and overall stages (p = 0.001) tended to be in the LN-LOR high-risk group, respectively. Notably, when we correlated LN-LOR risk groups with different affected anatomical LN locations, we found that patients with positive station 7 LNs tended to be more often in the LN-LOR high-risk group   . We did p = 0.01) and pN2 disease . Interestingly, in this cohort, the survival of pN1 and pN2 patients was similar  (p < 0.01), pN1 , pN2 ) . The results of a univariable survival analysis of standard prognostic factors are given in  = 0.79) A. Next, = 0.11)) A–C. Impo= 0.11)) B.p = 0.001)  .The traditional TNM staging system is a well established and reliable tool to guide treatment and estimate prognosis in NSCLC ,2,20,21.LN-LOR might be superior to the current N descriptors in reflecting the lymphatic disease burden and its influence on OS. As expected, we found that patients with higher pN stages and higher overall stages tended to be in the LN-LOR high-risk group. However, we also found that LN-LOR was additionally significantly associated with the presence of affected station 7 LNs. This finding might be partly explained by the mediastinal lymphatic vessel anatomy, where station 7 LNs could play a central role in mediastinal lymphatic cancer spreading and thus could be associated with a higher total number of affected LNs. To the best of our knowledge, this is the first study observing this possible association. Moreover, a recent study investigating 4797 patients with stage IIIA-N2 resected NSCLC from the Surveillance, Epidemiology and End Results (SEER) database has additionally shown that LN-LOR not only exhibited the best prognostic performance in predicting OS, but also could identify patients who benefit from PORT .Of note, we found that the survival of pN1 and pN2 patients was almost similar in our cohort of radically treated early-stage NSCLC. This finding is inconsistent with a large number of previous studies showing that pN2 is significantly associated with inferior prognosis compared to pN1. In contrary, however, other studies have clearly demonstrated that a subset of radically treated pN2 patients can have improved OS, with survival rates similar to stage II patients . FurtherIn the current TNM-8 staging system, it is suggested that at least six LNs from at least three different stations should be resected for adequate staging. However, according to various studies this is not an appropriate number ,33. AmonOur study has several limitations, partly due to its retrospective nature. Patients with different histological subtypes of NSCLC have been included, which could possibly bias our results. Furthermore, we have not taken the possible influence of tumor mutations by means of molecular pathology into account. In addition, information on recurrence was not available and recurrence-free survival could not be investigated. Lastly, the categorization of continuous variables, as performed in our study, bears the risk of loss of information and may be debated.In summary, this retrospective two-center analysis shows that LN-LOR is significantly associated with OS and might better reflect the biological behavior of the disease. According to our results, the anatomical location of involved LNs  might be inferior in estimating prognosis compared to the number of positive LNs. This finding may additionally support the value of extensive LN dissection.


The Centers for Medicare & Medicaid Services (CMS) recommend covering blood-based tests meeting proposed minimum performance thresholds for colorectal cancer (CRC) screening. Outcomes were compared between currently available stool-based screening tests and a hypothetical blood-based test meeting CMS minimum thresholds. Using the Colorectal Cancer and Adenoma Incidence and Mortality Microsimulation Model (CRC-AIM), outcomes were simulated for average-risk individuals screened between ages 45 and 75 years with triennial multitarget stool DNA (mt-sDNA), annual fecal immunochemical test (FIT), and annual fecal occult blood test (FOBT). Per CMS guidance, blood-based CRC screening was modeled triennially, with 74% CRC sensitivity and 90% specificity. Although not specified by CMS, adenoma sensitivity was set between 10% and 20%. Published adenoma and CRC sensitivity and specificity were used for stool-based tests. Adherence was set at (1) 100%, (2) 30%–70%, in 10% increments, and (3) real-world rates for stool-based tests . Assuming perfect adherence, a blood-based test produced ≥19 lower life-years gained (LYG) than stool-based strategies. At the best-case scenario for blood-based tests (100% adherence and 20% adenoma sensitivity), mt-sDNA at real-world adherence achieved more LYG  with 14% fewer colonoscopies. At 100% blood-based test adherence and real-world mt-sDNA and FIT adherence, the blood-based test would require advanced adenoma sensitivity of 30% to reach the LYG of mt-sDNA (297.1) and ∼15% sensitivity to reach the LYG of FIT (258.9). This model suggests that blood-based tests with CMS minimally acceptable CRC sensitivity and low advanced adenoma sensitivity will frequently yield inferior outcomes to stool-based testing across a wide range of adherence assumptions. Stool-based tests, including fecal immunochemical test (FIT), fecal occult blood test (FOBT), and multitarget stool DNA (mt-sDNA), detect markers released from both CRCs and precancerous adenomas8 The proposed minimum performance thresholds are 74% for CRC sensitivity and 90% for CRC specificity compared with colonoscopy. These thresholds are based on the minimum sensitivity and specificity for FIT and mt-sDNA.8 Interestingly, the recently issued CMS guidance does not stipulate performance thresholds for the detection of precancerous adenomas, a critical outcome measure that substantially impacts the effectiveness of screening modalities.8The Centers for Medicare & Medicaid Services (CMS) recommend covering blood-based biomarker tests with proposed minimum performance (sensitivity and specificity) thresholds for CRC screening test every 3 years for average-risk, asymptomatic Medicare beneficiaries of ages 50–85 years.To provide further clarity regarding the potential clinical utility of blood-based CRC screening under a number of potential clinical scenarios, microsimulation modeling analyses were conducted to predict life-years gained (LYG), CRC incidence, and CRC mortality outcomes resulting from blood-based CRC screening based on CMS minimum thresholds. These outcomes were then compared with available stool-based screening options .10 CRC-AIM has been previously validated to US CRC incidence and mortality presented in the Surveillance, Epidemiology, and End Results (SEER) database and cross-model validated against CRC Cancer Intervention and Surveillance Modeling Network (CISNET) models 11–14 that have been widely used for USPSTF16 and American Cancer Society5 guideline recommendations.Colorectal Cancer and Adenoma Incidence and Mortality Microsimulation Model (CRC-AIM) is a microsimulation model that incorporates assumptions for the known and unknown natural history of CRC and for CRC screening test parameters to simulate the impact of screening on CRC outcomes .9,10 CRC10The natural history component of the model follows the course of CRC using an adenoma–carcinoma sequence. The assumptions related to the natural history used in CRC-AIM have been previously described and include adenoma generation, adenoma growth, transition chance to preclinical CRC, CRC growth to become clinically detectable, and CRC death or survival, as shown in 9 It is assumed that when a screening test is positive, either any identified precancerous adenomas are removed to prevent the progression to preclinical CRC or that detection of early stage CRC is potentially more treatable, whereby mortality is reduced  for average-risk individuals free of diagnosed CRC at age 40 years and screened between ages 45 and 75 years. The age range of 45–75 years was selected per USPSTF findings that CRC screening among such individuals provides substantial or moderate net benefit.% to 20% .17 In 2 of the scenarios, adenoma sensitivity was assumed to be different for nonadvanced (<10 mm) and advanced (≥10 mm) adenomas  and advanced (≥10 mm) adenoma sensitivities for the blood-based test were conducted.https://github.com/CRCAIM/CRC-AIM-Public).Data were generated by the authors and are available on request. To promote full transparency and credibility of the CRC-AIM model, its formulas and parameters are available on a public repository (best-case scenario for blood-based tests was modeled (100% adherence and 20% nonadvanced and advanced adenoma sensitivity) and compared with real-world adherence rates for stool-based tests, mt-sDNA achieved more LYG  and high nonadvanced and advanced adenoma sensitivity (both 20%) are needed for the blood-based test to exceed the LYG of FIT at real-world adherence  with 40%Sensitivity analyses were conducted to determine what threshold of adenoma sensitivity would be required by a blood-based test to reach the same outcomes as mt-sDNA. At 100% adherence for the blood-based test and real-world adherence for mt-sDNA (65.6%), the blood-based test would require nonadvanced adenoma sensitivity of 20% and advanced adenoma sensitivity of 30% to reach the LYG of mt-sDNA 297.1; . At realand advanced (≥10 mm) adenomas, mt-sDNA had at least 21 higher LYG  given perfect adherence. The sensitivity assumptions of the stool-based tests to detect adenomas  were the same as those used by the USPSTF in their 2021 recommendations18 and were derived from data compiled from clinical studies.19 In their assessment of blood-based tests for CRC screening,8 CMS identified 2 studies that reported advanced adenoma sensitivity.23 These 2 studies used samples from the same patient population, but used first- and second-generation tests for which sensitivity was reported as 11% and 22%, respectively.23 In addition, preclinical results released for another blood-based test reported an advanced adenoma sensitivity of 20%,17 although results reported in December 2022 from the ECLIPSE trial indicate an advanced adenoma sensitivity of 13% for this blood test.24 Head-to-head trials will be the only way to determine whether the advanced adenoma sensitivity for a blood-based test is truly less than for stool-based tests. Therefore, for the current analysis, a lower limit for adenoma sensitivity was set at 10%, which is the false-positive rate of the CMS minimum threshold for specificity , and an additional range of adenoma sensitivities were applied up to a maximum of 20% to account for the full range of sensitivities at which the available blood-based tests are currently capable. The sensitivity analysis indicates that even when comparing the blood-based test at 100% adherence with mt-sDNA at real-world adherence, the blood-based test would require advanced adenoma sensitivity of 30% to achieve comparable clinical outcomes.The main driver of the scenarios wherein the blood-based test was inferior to the stool-based tests was the inability of the blood-based test to sufficiently detect advanced adenomas. Other models have also demonstrated the important contribution of the detection and removal of advanced adenomas in reducing CRC incidence and mortality.20 42.6% for FIT,21 and 34.4% for FOBT21 were applied in the model. Other adherence rates for the stool-based tests have been published that vary by the population evaluated and how adherence was defined.25–32 Therefore, a range of adherence rates from 30% to 100% were also applied in the model to accommodate the potential variability in real-world adherence.Screening for CRC needs to be repeated at intervals that vary depending on the screening test. Perfect adherence to the recommended 1- or 3-year intervals for noninvasive screening over the course of 30 years is not realistic in routine clinical practice. Thus, published real-world adherence rates of 65.6% for mt-sDNA,33 The results of a randomized controlled trial found that offering a blood test as a second-line screening option to patients who had previously declined FIT or a colonoscopy increased initial screening from 9.6% in the control group (reoffered FIT or colonoscopy) to 17.1% in the intervention group (offered blood test as second-line option).34 The rate of completing a full screening strategy  was not significantly different between the 2 groups. In contrast, another randomized controlled trial found that when nonparticipants of a previous CRC screening trial were offered FIT, a blood test, or a choice between the 2, there was no difference in participation rates among the groups and FIT was preferred in the choice group.35 With respect to the influence of screening adherence, 1 possible scenario is that real-world adherence to a blood-based test strategy will be higher than to a stool-based test strategy. Adherence may also depend on whether the blood-based test is recommended annually or triennially. In the current analysis, higher LYG with triennial mt-sDNA were observed compared with the triennial blood-based test (with 10% adenoma sensitivity), even when assuming adherence as low as 30% for mt-sDNA and as high as 100% for the blood-based test.Higher LYG with mt-sDNA were observed compared with the blood-based test (with 10% adenoma sensitivity) at all levels of adherence. When the levels of adherence were identical between the tests, mt-sDNA and FIT maintained an advantage in LYG over the blood-based test at the highest assumed adenoma sensitivity (20% for nonadvanced and advanced adenomas). The blood-based test at 20% adenoma sensitivity outperformed FOBT at lower adherence rates. The adherence to a triennial blood-based CRC screening test is unknown, but US self-reported screening surveillance data indicated that in 2009, 89% of adults aged 45–64 years in the general population had undergone blood cholesterol screening in the previous 5 years.36 The sensitivity to detect SSPs varies among CRC screening tests. In 1 study, mt-sDNA was more sensitive than FIT to detect SSPs measuring ≥10 mm .37 The sensitivity of blood-based tests to detect SSPs is unknown. Another limitation is that the current analysis was limited to evaluation of a 3-year interval for blood-based tests, in alignment with the recently proposed CMS guidance. Assessment of additional intervals may be informative and could be considered for future analyses.A limitation of this analysis is that the sensitivity of the blood- and stool-based screening tests to detect sessile serrated polyps (SSPs) was not specifically modeled. SSPs develop from a different genetic pathway than the conventional adenoma pathway, and ∼20%–30% of CRCs arise from this alternative pathway.In sum, data from this modeling study suggest that blood-based screening strategies leveraging tests with minimally acceptable sensitivity for CRC, coupled with low sensitivity for adenomas (using previously reported estimates), will yield clinical outcomes that are frequently inferior to stool-based strategies, even across a wide range of adherence assumptions. Clinical trials may yield different results than the outcomes simulated in this analysis, and head-to-head randomized trials are needed for verification. To realize meaningful public health benefits of CRC screening, strengths and weaknesses of various screening modalities must be conveyed to patients.


Guideline-recommended therapy for metastatic non-small cell lung cancer (mNSCLC) encourages evidence-based treatment; however, there is a knowledge gap regarding the influence of guideline-recommended initiation of therapy on outcomes and cost.To investigate if lack of guideline-recommended initiation of first-line systemic therapy was associated with worse patient outcomes and increased costs for patients with mNSCLC.In this retrospective analysis, 1,344 Medicare patients with mNSCLC were identified from Humana data. Performance status (PS) was imputed using procedure, diagnosis, and durable medical equipment codes pre-index. Guideline-recommended initiation of therapy was defined as ≥1 cycle of National Comprehensive Cancer Network-recommended first-line therapy based on age and PS or targeted therapies regardless of age and PS. Demographics and clinical characteristics were compared by guideline-recommended initiation of therapy. A Cox model assessed factors associated with 6-month mortality. End-of-life quality of care indicators included hospital admission and oncology infusions 30 days preceding death and were evaluated using logistic regression models. A generalized linear model assessed the relationship between guideline-recommended initiation of therapy and total health care costs in the 6 months post-index controlling for clinical, demographic, and treatment characteristics. Logistic models for inpatient stays and emergency department visits were also evaluated.P = 0.001), and more frequently identified as having a low-income subsidy . Among the 24.6% of patients who died ≤ 6 months post-index, a greater percentage had not initiated guideline-recommended therapy . In adjusted models, PS (not initiation of guideline-recommended therapy) was predictive of mortality (patients with poor PS had an 84% higher probability of death [P = 0.014]). Among decedents, 64.2% were hospitalized, and 33.9% had an oncology-related infusion within 30 days of death, with no differences by guideline-recommended initiation of therapy. These end-of-life quality indicators were not associated with guideline-recommended initiation of therapy in adjusted models. Overall, 47.5% of patients who initiated guideline-recommended therapy were hospitalized compared with 55.0% of patients who did not (P = 0.026). Patients initiating guideline-recommended therapy had higher post-index total and oncology-related health care costs and fewer hospitalizations. In models, these differences in costs and hospitalizations were not associated with initiation of guideline-recommended therapy.Guideline-recommended therapy initiation was observed in 75.5% of patients. Patients not initiating guideline-recommended therapy were older, with a mean (SD) age of 72.5 (6.7) versus 71.2 (6.2) years (Most patients initiated guideline-recommended therapy, with no differences in mortality and quality of care at the end of life by guideline-recommended initiation of therapy, though adherence beyond treatment initiation was not assessed. Unadjusted hospitalization rates were lower and costs were higher for patients who initiated guideline-recommended therapy. These differences were no longer observed after risk adjustment, suggesting that they may have been influenced by patient characteristics, disease progression, and subsequent treatment decisions. What is already known about this subjectPatients with advanced lung cancer and poor performance status (PS) have an increased risk of mortality.Treatment concordant with clinical guidelines improves the rates of morbidity and mortality for patients with non-small cell lung cancer (NSCLC).The lack of readily available PS data in administrative claims limits the ability to assess guideline-recommended initiation of first-line systemic therapy for metastatic NSCLC in larger multi-site national samples.What this study addsThis study used a previously validated classification model to approximate Eastern Cooperative Oncology Group PS using claims data as a mechanism to assess treatment initiation according to national guideline recommendations in a large national sample of patients with Medicare coverage.There were no differences in end-of-life care quality indicators by guideline-recommended initiation of first-line therapy, although adherence to therapy beyond initiation was not assessed.Lower mortality rates and higher health care costs observed in unadjusted models were no longer present after risk adjustment, suggesting that these differences were influenced by factors other than initiation of guideline-recommended therapy, such as hospitalizations and patient and clinical factors.2 Annually, lung cancer is diagnosed in approximately 235,000 patients in the United States,3 with non-small cell lung cancer (NSCLC) accounting for approximately 80%-85% of all lung cancer cases.4 Lung cancer is most often diagnosed after metastasis.5 Patients with lung cancer are generally elderly; the mean age at diagnosis is about 70 years. For patients with metastatic lung cancer, the 5-year survival rate is < 5%. In fact, more than half of patients with metastatic lung cancer die within a year of diagnosis.Lung cancer is the leading cause of cancer mortality for both men and women and is responsible for more than a quarter of all deaths attributed to cancer each year in the United States.8 Guideline recommendations for first-line treatment for metastatic non-small cell lung cancer (mNSCLC) consider patient age, tumor history, and Eastern Cooperative Oncology Group (ECOG) performance status (PS). PS is a measure of a patient’s functional capacity and has been found to predict survival in patients with cancer.10 NCCN guidelines balance the toxicity of a treatment with the age and ability of the patient to tolerate the treatment as indicated by PS. For patients with mNSCLC and good PS, the guidelines recommend chemotherapy.6 Palliative treatment is recommended in the elderly or for adults with poor PS.The National Comprehensive Cancer Network (NCCN) has developed evidence-based guidelines for many cancer types including NSCLC.12 These studies suggest that treatment adherent to clinical guidelines improved the rates of morbidity and mortality for patients with NSCLC; however, studies have also reported inconsistent adherence to various aspects of the clinical guidelines. For example, in a study considering concordance with NCCN guideline recommendations for initiation of first-line therapy for mNSCLC, Zornosa et al. (2012) found that 76% of patients received care consistent with NCCN guidelines.12 Another study by Wang et al. (2013) demonstrated that adherence rates to guidelines for adjuvant and first-line treatment of NSCLC were 61% and 75%, respectively.11 Moreover, for patients with advanced NSCLC, adherence to guidelines for first-line treatment was correlated with a longer follow-up monitoring period. Salloum et al. (2012) reported similar results in patients > 50 years old in that 71% received treatment based on current guidelines.13 The authors also suggested that the 29% of patients who did not receive guideline-adherent therapy missed possible benefits of recommended therapy or received treatment associated with greater risk of harm than benefit.Previous research has evaluated adherence to NCCN treatment guidelines for NSCLC and the association between guideline-adherent treatment and morbidity and mortality.13 the lack of PS in medical claims has limited the ability to conduct larger, more generalizable studies of adherence to NSCLC treatment guidelines. Further, the physician assessment of PS utilized in these studies could have been subject to provider bias, where PS estimates may have been upgraded to justify treatment. To provide a less biased and more generalizable assessment of the effect of initiating first-line therapy according to guideline recommendations, our study adapted a classification model for imputing PS from claims data.15 The association between guideline-recommended initiation of first-line therapy and outcomes and cost was assessed; however, ongoing adherence to the guideline-recommended therapy was not evaluated.15 Specifically, this retrospective claims analysis investigated if lack of first-line systemic therapy initiation according to guideline recommendations was associated with worse patient outcomes and increased health care costs for patients with mNSCLC and Medicare coverage.In the assessment of guideline treatment adherence, these studies obtained PS from medical charts, were conducted in single institutions, and were limited to smaller sample sizes. As noted by Salloum et al. (2012),Data were obtained from 2 sources for this retrospective cohort analysis: preservice consultation regarding biologic or chemotherapeutic infusions and administrative enrollment and medical and pharmacy claims data. The Humana Research Database was utilized for patients with Medicare Advantage Prescription Drug plan (MAPD) coverage. The database contained patient characteristics, diagnoses, and procedures, including chemotherapy administration. Preservice consultation data contained information that providers submitted as a request for consultation regarding biologic or chemotherapeutic infusions and included clinical factors pertinent to cancer diagnosis and treatment not available in claims data.The research protocol was approved by an independent institutional review board (Schulman IRB).To be included in the study, patients with NSCLC enrolled in an MAPD must have been between 50 and 89 years of age at the first infusion, had a first-line NCCN-recommended drug regimen for NSCLC , and had documentation of at least 1 refill for drugs initiated on days 1-8 of first-line therapy. The refill requirement was intended to capture the regimen at initiation of therapy. The index date was the date of first treatment. Patients must also have been continuously enrolled 6 months pre- and post-index or until death. Patients participating in a clinical trial during the study period, as indicated in preservice consultation requests, were excluded.International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnostic codes, and there is no specific ICD-9-CM code for NSCLC. However, there is a reliable ICD-9-CM code that can be used to ascertain metastatic status from claims data. Metastatic status was also available in preservice consultation data as reported by providers. As such, both preservice consultation data and claims were considered to determine if the NSCLC diagnosis was metastatic. Metastatic status in claims was identified as having at least 2 medical claims with ICD-9-CM codes for metastatic status  or good/moderate PS (ECOG PS = 0-2) based on the predicted likelihood of disability. In order to evaluate the initiation of guideline-recommended therapy, it was necessary to delineate moderate PS (ECOG PS = 2) from the disability model’s assessment of good/moderate PS, which approximated ECOG PS of 0, 1, or 2. We estimated moderate PS (ECOG PS = 2) using the top quintile of the predicted likelihood of disability for patients with imputed good/moderate PS. A sensitivity analysis was conducted to determine if discrimination could be improved using either the top quartile or top sextile.In order to assess adherence to treatment guideline recommendations, ECOG PS was approximated using claims data. Davidoff et al.  explored the use of disability status as a proxy for ECOG PS.First-line NCCN guideline-recommended treatment categories included platinum doublet chemotherapy, single-agent chemotherapy, bevacizumab and chemotherapy, targeted agents, and other regimens. Treatment was identified in medical claims using procedure codes from the Healthcare Common Procedure Coding System HCPCS; . Any treGuideline-recommended initiation of therapy was defined as having claims evidence of at least 1 cycle of the applicable NCCN-recommended nontargeted first-line therapy based on age and imputed PS , and revenue codes. The use of oxygen was identified in inpatient and outpatient medical claims using HCPCS codes (Comorbidity was measured using the Klabunde-enhanced Deyo-Charlson Comorbidity Index (CCI).CS codes .19Death  within 6 months was identified based on the date of death provided to the health plan from the Centers for Medicare & Medicaid Services. Quality of care at the end of life was measured as having biologic or chemotherapy infusions within 30 days of death or having an inpatient stay within 30 days of death.Post-index health care utilization was compared for patients initiating guideline-recommended therapy to those who did not initiate guideline-recommended therapy. Health care resource utilization was evaluated as the percentage of patients having 1 or more all-cause hospitalization and 1 or more all-cause emergency department visit in the 6-month post-index period.All cost measures were health plan total allowed costs and included costs incurred by both the patient and the health plan. Total all-cause health care costs were defined as the sum of costs related to all medical and pharmacy claims occurring within 6 months post-index. Oncology-related costs included costs accrued at all places of service for oncology-related care including surgical procedures, infusions and infusion administration, radiation, diagnostics, imaging, and inpatient stays as identified by CPT and ICD-9-CM codes. Total oncology-related health care cost was the sum of all medical and pharmacy claims related to oncology treatment post-index. All health care costs were adjusted to 2015 U.S. dollars based on the medical care component of the Consumer Price Index.We compared the characteristics of patients who initiated first-line therapy according to guideline recommendations with those who did not using chi-square tests for categorical variables. T-tests and Wilcoxon tests were used for continuous variables.In order to assess the effect of initiating guideline-recommend therapy on 6-month all-cause mortality, a multivariable Cox proportional hazards model was executed. The Cox model controlled for the following factors: age, sex, low-income Part D subsidy, Deyo-CCI, PS, pre-index use of radiation, and treatment initiation with platinum doublet chemotherapy. Results of the Cox model are reported as hazard ratios with a 95% confidence interval.To evaluate quality of care at the end of life for patients who died within 6 months post-index, 2 multivariable logistic regression models were used to assess the relationship between guideline-recommended initiation of first-line therapy having (a) an oncology-related infusion or (b) an inpatient stay in the last 30 days of life. These models were adjusted for the same variables used in the all-cause mortality model.Two distinct multivariable logistic regression models were evaluated to determine if there was an increased odds of 1 or more post-index hospitalizations or emergency department visits by the initiation of guideline-recommended treatment. The models controlled for variables that may also affect hospitalization or emergency department visits, including pre-index utilization, age, sex, geographic region, low-income Part D subsidy, death during post-index, Deyo-CCI, imputed PS, and first-line treatment regimen category. In addition, the number of post-index months of observation was included to accommodate variations in follow-up time in the model, such as that of decedents.The association between initiation of guideline-recommended therapy and total all-cause health care cost within 6 months post-index was evaluated using a generalized linear model (GLM). The model controlled for log of pre-index all-cause health care costs and covariates included in the logistic regression model predicting utilization. Patients who initiated hospice during the post-index period were excluded from this model as hospice claims are incomplete in Medicare Advantage data. All statistical tests were 2-tailed and conducted in SAS Enterprise Guide, version 7.1 . Statistical significance was determined using a significance level of 0.05 established a priori.P = 0.009 and P = 0.003, respectively).For the 1,344 patients with mNSCLC, imputed PS was assigned as good for 76.2% of patients, moderate for 18.5% of patients, and poor for 5.3% of patients. To assess the discrimination of the imputed PS classification, the proportion of patients who died or initiated hospice in the 6 months post-index was compared by imputed PS level . The freGuideline-recommended first-line therapy was initiated by 75.5% of patients with mNSCLC . The meaP < 0.001).Better PS was observed among patients with guideline-recommended initial therapies . Among pP = 0.040). In an adjusted Cox model, there was no difference in 6-month all-cause mortality between patients who initiated guideline-recommended therapy and those who did not (Nearly 25% (n = 330) of patients died within 6 months post-index . In unad did not . HoweverP = 0.451). There also was no difference in having oncology-related infusions in the last 30 days of life by guideline-recommended initiation of therapy. In multivariable logistic regression models, the odds of having an inpatient stay or having an oncology-related infusion in the last 30 days of life were not influenced by whether or not the initiation of first-line therapy aligned with guideline recommendations . In multivariable regression models, these utilization differences were not attributable to the initiation of treatment but to higher post-index mortality and pre-index utilization among patients who did not initiate treatment according to guideline recommendations . Total oncology-related costs were $35,679  for patients who initiated guideline-recommended treatment compared with $31,054  for patients who did not initiate guideline-recommended therapy (P = 0.003). There were no differences in outpatient or pharmacy costs in unadjusted analyses. An adjusted GLM assessing factors associated with post-index total all-cause health care costs found no association between initiating treatment according to guideline recommendations and total all-cause health care costs  and health care utilization and costs for patients with mNSCLC. In adjusted analyses, no differences in outcomes or costs based on the initiation of guideline-recommended first-line systemic therapy for mNSCLC were found. However, in unadjusted analyses, there was an increased rate of inpatient stays for patients who did not initiate therapy according to guidelines. While increased mortality and costs for patients whose treatment initiation was not in alignment with guideline recommendations were found in unadjusted analyses, these differences were no longer present in adjusted multivariable models. This suggests that factors associated with the determination of the initiation of guideline-recommended therapy—and not the therapy itself— influenced patient outcomes and health care costs.12 Despite the differences in study design, the current study found that 75.4% of patients initiated first-line systemic treatment in alignment with NCCN guideline recommendations.Using real-world data from a national cohort, we focused on the initiation of first-line systemic treatment rather than the trajectory of the entire course of treatment. In a study of patients with mNSCLC treated at NCCN institutions, Zornosa et al. (2012) found that 75.5% of patients had treatment congruent with NCCN guideline recommendations.12 The current study took place after erlotinib and pemetrexed were added to the guidelines. In this study, systemic treatment that was not initiated according to NCCN guideline recommendations was most commonly found among patients with poor PS. For these patients, the guideline recommendation was a palliative approach to treatment6; however, maintenance measures were observed. Providers may partially base these treatment decisions on findings such as Feliciano et al. (2015), who found that patients with mNSCLC and poor PS derived a survival benefit from chemotherapy but not to the same extent as patients with good PS.16Additionally, Zornosa et al. evaluated the reasons for mNSCLC treatment discordant with guideline recommendations. Their study showed that among patients with good or moderate PS, the most commonly observed reasons for treatment misalignment with NCCN recommendations were that it was not recommended by the provider or the patient died before treatment began. For the remainder of patients, the most common reason that treatment diverged from guideline recommendations was that patients were treated with erlotinib or pemetrexed before these treatments were added as first-line treatment options in the guidelines.20 The lack of association between initiation of guideline-recommended therapy and mortality in our study may suggest that improvements in mortality rates are found when the continuity of treatment, beyond initiation of first-line treatment, is congruent with NCCN guideline recommendations. As with previous research, our study found that mortality rates were increased for patients with poor PS compared with those with good PS at the initiation of treatment.10The current study found no association between the initiation of guideline-recommended therapy and mortality in multi-variable models for patients with mNSCLC, though adherence to guideline-recommended initial therapy was not assessed. Treatment guidelines are intended to promote evidence-based practice in order to optimize patient outcomes, including reducing the risk of mortality. The association between clinical guideline concordance and improved mortality rates has been established previously.19 Among those indicators were (a) continuation of ongoing treatments near death, (b) inpatient hospital stays near death, and (c) percentage of patients never initiating hospice. Similar quality of care measures at the end of life were evaluated in the current study by initiation of NCCN guideline-recommended first-line treatment. In adjusted analysis, no association was found between initiating therapy according to guideline recommendations and an inpatient stay or oncology-related infusion in the last 30 days of life. Additionally, hospice initiation rates did not differ by initiation of guideline-recommended treatment. Active treatment of patients with poor PS has been shown to improve survival, although there are concerns about the effect of treatment tolerability and toxicity on health-related quality of life.Claims-based indicators to assess the quality of care at the end of life for patients with cancer have been proposed by Earle et al. (2010).21 This study was able to extend the evaluation of the association between the initiation of guideline-recommended therapy and health care cost beyond outpatient costs to total all-cause health care costs. Unadjusted post-index costs were higher for patients with initial therapy by guideline recommendation for mNSCLC patients; however, these differences were explained by patient and treatment characteristics in adjusted multivariable analyses. Differences in all-cause total health care cost between patients with guideline-recommended and nonguideline-recommended treatment initiation may be attributable to patient-level characteristics and prescribed treatment regimens. These findings may suggest that factors influencing initiation of guideline-recommended therapy—and not the initiation of therapy itself—may lead to cost differentials within this population.Neubauer et al. (2010) found that the initiation of guideline-recommended therapy for mNSCLC was associated with lower outpatient costs.This study has limitations that need to be considered. Patients with NSCLC were identified for this study using physician requests for preservice consultation regarding the initiation of first-line biologic or chemotherapeutic infusion therapy. Patients with NSCLC not referred for infusion therapy, including those on oral treatment only, were not included in this study, potentially affecting the generalization of the study findings. This study considered guideline-recommended therapy at initiation of treatment and did not assess adherence beyond initiation of therapy.15; however, the modification to distinguish moderate PS from the imputed good/moderate PS has not been validated. The Davidoff disability model has been validated with mortality and initiation of cancer treatment but not directly compared with ECOG PS, although the measure may more broadly capture markers of decreased functional status as it considers use of durable medical equipment and other indicators of the sequelae of disability. We found differences in PS by guideline-recommended initiation of therapy; this may be due to unexplained clinical differences between the cohorts.PS was imputed from a validated classification modelFurther limitations include that the latest immunotherapies and targeted therapies for genotype mutations were introduced after the study time period, precluding them from inclusion in this study. These therapies may be appropriate for mNSCLC patients and need to be considered in future research efforts. In addition, utilization and cost data after a patient initiates hospice may be incomplete as they are not all administered by Medicare Advantage. Finally, there was no adjustment applied for costs at the end of life for patients who died during the post-index period to account for the expected rise in health care costs at the end of life.Most patients initiated guideline-recommended therapy, with no differences in mortality and quality of care at the end of life by guideline-recommended initiation of therapy, although adherence beyond treatment initiation was not assessed. Unadjusted hospitalization rates were lower and costs were higher for patients who initiated guideline-recommended therapy. These differences were no longer observed after controlling for patient characteristics, suggesting that they may have been influenced by patient characteristics, disease progression, and subsequent treatment decisions. The findings of this study may suggest that patient outcomes are more closely associated with patient characteristics and the totality of treatment beyond the initiation of first-line therapy in accordance with guideline recommendations.


The COVID-19 pandemic sparked the development of novel anti-viral drugs that have shown to be effective in reducing both fatality and hospitalization rates in patients with elevated risk for COVID-19 related morbidity or mortality. Currently, nirmatrelvir/ritonavir (Paxlovid™) fixed-dose combination is recommended by the World Health Organization for treatment of COVID-19. The ritonavir component is an inhibitor of cytochrome P450 (CYP) 3A, which is used in this combination to achieve needed therapeutic concentrations of nirmatrelvir. Because of the critical pharmacokinetic effect of this mechanism of action for Paxlovid™, co-administration with needed medications that inhibit or induce CYP3A is contraindicated, reflecting concern for interactions with the potential to alter the efficacy or safety of co-administered drugs that are also metabolized by CYP3A. Some herbal medicines are known to interact with drug metabolizing enzymes and transporters, including but not limited to inhibition or induction of CYP3A and P-glycoprotein. As access to these COVID-19 medications has increased in low- and middle-income countries (LMICs), understanding the potential for herb-drug interactions within these regions is important. Many studies have evaluated the utility of herbal medicines for COVID-19 treatments, yet information on potential herb-drug interactions involving Paxlovid™, specifically with herbal medicines commonly used in LMICs, is lacking. This review presents data on regionally-relevant herbal medicine use (particularly those promoted as treatments for COVID-19) and mechanism of action data on herbal medicines to highlight the potential for herbal medicine interaction Herb-drug interaction mediated by ritonavir-boosted antiviral protease inhibitors This work highlights potential areas for future experimental studies and data collection, identifies herbal medicines for inclusion in future listings of regionally diverse potential HDIs and underscores areas for LMIC-focused provider-patient communication. This overview is presented to support governments and health protection entities as they prepare for an increase of availability and use of Paxlovid™. The development of novel anti-viral drugs represents a critical step forward in the global control of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), also known as COVID-19. Access to these medications for use by patients at elevated risk for COVID-19-related morbidity or mortality has dramatically lowered hospitalization and fatality rates, and reduced the overall burden of illness as well . Of thesHypericum perforatum), is included in the list of contraindicated substances highlighted in the FDA or EMA fact sheets for healthcare providers . Ritonavir acts as a pharmacokinetic boosting agent for other antiviral agents contained in both HAART and Paxlovid™ by inhibiting the prominent drug-metabolizing enzyme cytochrome P450 (CYP) 3A, facilitating the achievement of therapeutic concentrations by other co-administered antiviral drugs metabolized by CYP3A . Becauseroviders . As Paxlroviders  and promroviders . Althougroviders , well chroviders . ConceptTo the authors’ knowledge, this review is the first to present data on regionally relevant herbal medicine use (particularly those promoted as treatments for COVID-19) in LMICs and mechanistic data on herbal medicines to discuss the potential for ritonavir-boosted antiviral protease inhibitor-mediated HDIs. This work highlights potential areas for future experimental studies and data collection, identifies herbal medicines for inclusion in future listings of regionally diverse potential HDIs, and suggests areas for LMIC-focused provider-patient communication. This summary aims to support governments and health protection entities as they prepare for the global availability and use of Paxlovid™.Because the current Emergency Use Authorizations in the United States for Paxlovid™ are focused on CYP3A-mediated interactions , we direThis work is not a systematic review of potential novel HDIs with COVID-19 treatments but rather presents data derived from a limited pool of clinical and nonclinical studies for herbal medicines identified as commonly used in parts of Africa or Asia, or those used or proposed for use in the treatment of COVID-19 in these regions. This review draws on data from the peer-reviewed literature, as well as expert opinion from pharmacognosists, pharmacologists, toxicologists, researchers—including in-country experts in these areas—to inform initial case studies on potential HDIs involving herbal medicines and Paxlovid™ in low and middle-income regions in Africa and Asia. The description of plants as treatments in this review are not meant to support the reported efficacy of the plants, but to describe their use and discuss potential interactions . Herbal The author team identified a list of herbal medicines and plants for potential inclusion in this article based upon the following criteria:• Expert opinion of author team based on local practices and availability in their county or region.• Initial data of common use in LMICs in Africa and Asia.• Initial data of promotion or use specifically as a treatment for COVID-19.in vitro, in vivo, or clinical data).• Potential for pharmacokinetic interactions with Paxlovid™ based on available mechanistic or clinical evidence -testosterone) in Caco-2 cell monolayers, whereas artemisinin alone had no effect  and CYP3A4 (testosterone 6β-hydroxylation) in human liver microsomes, suggesting the potential for HDIs if these concentrations are attained in vivo. Studies have reported that all parts of B. volubilis contain alkaloids and several cardiac glycosides of the scillaren type   (Turmeric is obtained from the rhizomes of isorders . Turmeriisorders . The rhi weight) . Some prectively . Althougectively . A cliniectively . Several (ABCC3) . TherefoGlycyrrhizae radix et rhizoma , also known as licorice, is the dried root and rhizome of Glycyrrhiza uralensis Fisch., Glycyrrhizae inflata Bat, or Glycyrrhizae glabra L, and is a commonly used herbal medicine native to West Asia, North Africa, and Southern Europe  and extracts from the plant have shown potent anti-rheumatic, anti-inflammatory and analgesic effects  cell line.    noted thHypoxis haemercollidea Fisch., C.A. Mey. (Hypoxidaceae), also known as African potato, is a tuberous perennial with star shaped yellow flowers that is indigenous to Southern Africa   . Many sp similar . Of thesodulator  and is aodulator . Hypoxosodulator . Other nodulator . HDIs ind CYP3A4 , as welld CYP3A4 . Africand CYP3A4 . For extd CYP3A4 , whereascollidea  comparedcollidea . Co-admicollidea  nor lopicollidea , which aLessertia frutecens (L.) Goldblatt & J.C.Manning (previously Sutherlandia frutecens (L.)., R.Br) also known as cancer bush or Sutherlandia, is a flowering shrub that is a member of the Fabaceae family  and activation of hPXR (via CYP3A4 luciferase reporter gene construct) and inhibited P-gp activity (via orthovanadate sensitive release of phosphate and adenosine triphosphatase activity) in HepG2 cells ; however, after 5 days of exposure, CYP3A4 activity increased 2-3 fold (max) decreased by 50% after 5 days of treatment with Lessertia (max of atazanavir decreased in human participants (n = 12) after a single dose of Sutherlandia tablets  in the time for amodiaquine to reach Cmax. Several moringa phytochemicals have demonstrated antispasmodic activity, including glucomorigin, which may slow absorption, thus increasing time to reach Cmax. In addition, other phytochemicals could form insoluble complexes affecting both Cmax and the time to reach Cmax  has shown beneficial effects against inflammation and viral infection, including COVID-19  relative to control  and incin vitro and in silico data are available for HDIs involving herbal medicines such as V. amygdalina, and L. frutecens. However, how these data and results translate to the clinical settings remain unknown. As Paxlovid™ is scaled up globally and the concurrent use of antivirals and herbal medicines increases, additional research can help elucidate the clinical impact of HDIs. Prospective and retrospective studies can add to the understanding of HDIs. Predictive modelling based on available non-clinical data by applying established tools  may also inform HDIs with Paxlovid. Without this critical information, adverse events can result, and systemic concentrations can vary , which may lead to drug resistance, low patient adherence, and failed therapy.Extensive Study limitations include a non-systematic review methodology, however, the information provided in this manuscript raises awareness of potential HDIs and is not meant to be a comprehensive review.With the likelihood of future SARS-CoV-2 variants emerging, Paxlovid™ represents an effective therapeutic tool to prevent death and strain on healthcare systems. Raising awareness and furthering the understanding of HDIs is crucial as the global Paxlovid™ rollout continues.


Heart failure with preserved ejection fraction (HFpEF) and obstructive sleep apnea (OSA) frequently co-occur and this comorbidity represents a separate phenotype of HFpEF. While many research attempts are focused on biomarkers of HFpEF, currently, there is a lack of validated biomarkers of HFpEF and OSA. In this study, we aimed to evaluate prognostic significance of several serum cytokines in patients with HFpEF and OSA. The patients with HFpEF and OSA were recruited from the Sleep Apnea Center of Novosibirsk, Russian Federation and followed up for 12 months. The main analyzed outcomes were five-point major adverse cardiovascular events (MACE) and the 6-min walk test (6MWT). The analyzed cytokines were circulating IL-6, IL-10, and VEGF measured at baseline. We recruited 77 male patients with HFpEF and OSA, the data of 71 patients were available for analyses. Patients who developed MACE had four-fold elevated concentrations of serum IL-10. There was no association between baseline cytokine levels and longitudinal changes in 6MWT. Circulating IL-10 levels are positively associated with MACE in men with HFpEF and OSA and thus may be a potential prognostic biomarker in this subgroup of patients. These results should be confirmed in larger studies encompassing both males and females. Heart failure (HF) is a significant health problem with prevalence reaching 1–2% of the adult population in the Western world . HFrEF aHF is frequently accompanied with different comorbidities which can further impair prognosis of this condition . One of There is a need for novel biomarkers for both HF  and OSA Given the role of cytokines in inflammation and angiogenesis in both HF  and SDB The study design was a prospective cohort study. The study protocol was approved by the Local Ethics Committee attached to the Clinical Hospital of Rossiyskie Zheleznye Dorogi, approval number 76. All patients provided written informed consent.The patients were recruited from a population of male railroad workers from Novosibirsk Oblast attending an annual required medical checkup in the period from 2017 to 2019. People having three risk factors for OSA  were then referred to the Sleep Apnea Center for further evaluation. In order to improve efficiency of the screening, we did not use common screening tools like STOP-BANG, as in some studies, it lacked good performance in younger patients . Thus, wThe people attending the Sleep Apnea Center were invited to participate in the study. The patients were included in this study if, on baseline visit, they had confirmed HFpEF and OSA and fulfilled the following criteria:(1)Symptoms of HF, New York Heart Association (NYHA) Functional Classification class I–II;(2)Moderate to severe OSA (Apnea–Hypopnea Index (AHA)) > 15 in hour;(3)Arterial hypertension;(4)Abdominal obesity N-terminal (NT)-pro hormone BNP (NT-proBNP) > 125 pg/mL.Reduced (≥50%) left ventricular ejection fraction;Primary pulmonary hypertension;History of pulmonary embolism with pulmonary hypertension ≥ 45 mm Hg;Severe asthma or COPD;Significant valvular abnormality;Hypertrophic or dilated cardiomyopathy;Coronary artery disease;Persistent atrial fibrillation;2;Thyroid disease, renal failure with creatinine clearance < 30 mL/mSignificant CSA (≥15 episodes of CSA in hour).To diagnose OSA, all patients underwent polysomnography using the Somnolab2PSG diagnostic system . We used the Apnea Hypopnea Index (AHI) to assess the severity of OSA. To evaluate serum cytokines levels, we used the enzyme-linked immunosorbent assay (ELISA). Echocardiography was performed in all patients using standard protocol on the EPIQ device .The ELISA analyses were performed using commercial ELISA kits . Based on the manufacturer’s instructions, the detection limits for IL-6, IL-10, and VEGF were 0.5 pg/mL, 2.5 pg/mL, and 10 pg/mL, respectively. Concentrations below these thresholds were considered non-detects.The assessments were made on the baseline visit and on 12 months follow-up. On the baseline visit, we evaluated clinical and demographic parameters, polysomnography, echocardiography, 6-min walk test, and serum cytokine levels. On the follow-up visit, we re-performed all assessments except serum cytokines.In this study, we assessed the following outcomes: five-point Major Adverse Cardiac Events (MACE) (primary endpoint) and six-minute walk test (6MWT) (secondary endpoint). The reason for choosing MACE as an outcome is that it is one of the most commonly used composite endpoints (CE) in both epidemiological studies and clinical trials on HF . CE is dTotal death;Myocardial infarction;Stroke;Hospitalization because of HF;Revascularization, including percutaneous coronary intervention, and coronary artery bypass graft.The criteria of five-point MACE were as follows :Total deThe 6MWT was performed according to the previous guidelines . We adviWe instructed all patients to walk as fast as they could along the corridor. They were also informed to slow down their walk or to interrupt it if necessary. During the tests, we monitored the patients verbally by the Borg modified scale every 2 min. By the sixth minute, we requested each subject to stop and the number of runs and the remaining distance of the last run were summed. Before the test and during the first and sixth minute after finishing the test in the sitting position, we measured the blood pressure and H. Two tests were performed with a 30-min interval, and the average of the two 6MWD values was used to best approximate a representative true value.Serum cytokine values usually have a substantial proportion of non-detects. For a particular cytokine, we considered the concentrations below the reporting threshold as non-detectable and further treated them as left-censored. We performed Tobit regression, a recommended statistical approach for left-censored data  for each2) and abdominal (waist circumference > 92 cm) obesity. All of the patients were diagnosed with NYHA grades I-II HF.The baseline characteristics of included patients are found in During the follow-up, 14 (19.17%) patients developed MACE. The incidence of individual components of MACE are presented in As shown in In this study, we found increased circulating IL-10 levels in patients with HFpEF and OSA who later developed MACE during the follow-up. There were no links between studied serum cytokines and physical function as measured by 6MWT.To our knowledge, this was the first study evaluating serum cytokine biomarkers in a subset of patients with HFpEF and comorbid CSA.Circulating cytokine levels have been assessed in many cross-sectional studies on OSA or HF. A recent meta-analysis showed increased circulating IL-6 levels in patients with OSA . ReducedAlthough many studies cross-sectionally assessed serum cytokines in HF and OS, there has been a lack of research prospectively evaluating prognostic value of IL-6, IL-10, and VEGF.Our results conflict with the findings of the BIOSTAT-CHF study, showing IL-6 to be a predictor of worse CV outcomes . The difOur findings are in line with the studies showing impaired CV outcomes in patients with elevated serum IL-10 ,41,42. TFrom the first glance, the absence of a link between baseline cytokines and longitudinal changes in 6MWT contradicts our findings on elevated IL-10 in patients who had developed MACE. This discrepancy can be explained by suboptimal performance of 6MWT as an outcome in HF patients. Thus, a meta-analysis showed 6MWT improvement in only nine of 47 randomized controlled trials of pharmacological therapy in HF .Our study has several important limitations: it was a single-center study, performed on a rather small sample of middle-aged men. These might restrict the generalizability of our findings.The strengths of our study are that it had a longitudinal design and that we performed a statistical analyses accounting for left censoring inherent to immunological data.This study represents the first screening step in evaluating IL-10 as a candidate predictor biomarker of adverse CV outcomes in patients with HFpEF with OSA. The future studies on larger populations will need to confirm these results. If IL-10 is confirmed to be significantly elevated in patients with increased incidence of negative CV outcomes, further research will have to evaluate the effectiveness of serum IL-10 as a predictive biomarker. Determining clinical cutoffs using receiver operating characteristics analysis will be necessary to allow using serum cytokines as biomarkers in clinical practice.An “ideal” predictive biomarker should be noninvasive, inexpensive, and effective . The firIn spite of these challenges, there is still a hope that some cytokines can be used as effective biomarkers for some diseases in the future. The reason for this hope is that there are at least several probable ways to overcome the said difficulties: using a combination of several cytokines to build more accurate predictive models, establishing valid reference cytokine concentrations on large populations of healthy people, and a rigorous standardization of the measurement techniques ,52.In conclusion, higher serum concentrations of IL10 in men with HFpEF and CSA are associated with MACE during the follow-up. These findings need to be replicated on a more general population with a larger sample size.


Learning effective molecular feature representation to facilitate molecular property prediction is of great significance for drug discovery. Recently, there has been a surge of interest in pre-training graph neural networks (GNNs) via self-supervised learning techniques to overcome the challenge of data scarcity in molecular property prediction. However, current self-supervised learning-based methods suffer from two main obstacles: the lack of a well-defined self-supervised learning strategy and the limited capacity of GNNs. Here, we propose Knowledge-guided Pre-training of Graph Transformer (KPGT), a self-supervised learning framework to alleviate the aforementioned issues and provide generalizable and robust molecular representations. The KPGT framework integrates a graph transformer specifically designed for molecular graphs and a knowledge-guided pre-training strategy, to fully capture both structural and semantic knowledge of molecules. Through extensive computational tests on 63 datasets, KPGT exhibits superior performance in predicting molecular properties across various domains. Moreover, the practical applicability of KPGT in drug discovery has been validated by identifying potential inhibitors of two antitumor targets: hematopoietic progenitor kinase 1 (HPK1) and fibroblast growth factor receptor 1 (FGFR1). Overall, KPGT can provide a powerful and useful tool for advancing the artificial intelligence (AI)-aided drug discovery process. Accurate property prediction relies on effective molecular representation. Here, the authors introduce KPGT, a knowledge-guided self-supervised framework that improves molecular representation, leading to superior predictions of molecular properties and advancing AI-driven drug discovery. In recent years, artificial intelligence (AI)-based approaches have played an increasingly pivotal role in predicting molecular properties, offering remarkable efficiency and cost-effectiveness5. One of the primary challenges of AI-based approaches for molecular property prediction is the representation of molecules7. The early machine learning-based approaches involved preliminary attempts toward representing molecules using basic handcrafted features10. Among these, the most prominent ones include molecular descriptors13, which quantitatively characterize the physical and chemical profiles of small molecules, and fingerprints16, which utilize binary strings to signify the presence of specific substructures within a molecular structure. The prediction methods based on these representations are highly dependent on complicated feature engineering strategies, consequently compromising their generalizability and flexibility.The identification of molecules with desired properties presents one of the most significant challenges in the drug discovery field, given the considerable time and resources required for experimentally determining molecular properties19, convolutional neural networks (CNNs)22, and graph neural networks (GNNs)26, excel in modeling molecular data across various formats, from the simplified molecular-input line-entry system (SMILES)27 to molecular images and molecular graphs. Nevertheless, the limited availability of labeled molecules and the vastness of the chemical space have constrained their prediction performance, particularly when handling out-of-distribution data samples29. Along with the remarkable achievements of self-supervised learning methods in the fields of natural language processing31 and computer vision33, these techniques have been employed to pre-train GNNs and improve the representation learning of molecules, leading to substantial improvements in the downstream molecular property prediction tasks42.Recent years have witnessed the emergence of deep learning-based methods as potentially useful tools for predicting molecular properties, primarily due to their remarkable capability of automatically extracting effective features from simple input data. Notably, a diverse range of neural network architectures, including recurrent neural networks (RNNs)36, GROVER38, and GEM42, typically involve the modification of molecular graphs by means of node or subgraph masking, followed by the prediction of the masked components38, or the utilization of contrastive learning objectives to align the modified graphs with their corresponding originals in latent space37. Molecules inherently possess characteristics tightly linked to their structures, which implies that even minor modifications of molecular graphs can lead to the loss of their semantic information. This natural characteristic of molecules potentially limits current self-supervised learning-based methods for molecular graphs to capture structural similarity among molecules and fails to capture the rich semantic information related to molecular properties encoded within their chemical structures 43. Moreover, in the absence of semantic information on molecular graphs, the only dependency between the masked nodes and their adjacent nodes is the valency rules, which often fail to guide the model to make accurate predictions for the masked nodes  as backbone models. However, GNNs can only provide limited model capacity, as they suffer from over-smoothing when increasing their numbers of layers47. Additionally, GNNs might struggle to capture long-range interactions between atoms, arising from their standard practice of exchanging information only among one-hop neighbors during the message passing process49. Recent advancements in backbone networks, particularly transformer-based models50, have emerged as game-changers52. These models, characterized by an increasing number of parameters and the capability to capture long-range interactions, present promising avenues to comprehensively model the structural characteristics of molecules57.Existing self-supervised learning methods generally rely on GNNs , particularly designed to accurately model molecular graph structures, with a knowledge-guided pre-training strategy aiming to capture both structural and semantic knowledge of molecules. After pre-training on a large-scale dataset consisting of approximately two million molecules, KPGT demonstrated a significant performance enhancement on 63 molecular property datasets. Moreover, we showcased the practical applicability of KPGT by successfully utilizing it to identify potential inhibitors for two antitumor targets, hematopoietic progenitor kinase 1 (HPK1) and fibroblast growth factor receptor (FGFR1). In summary, KPGT offers a powerful self-supervised learning framework for effective molecular representation learning, thereby advancing the field of AI-aided drug discovery.In this study, we introduce KPGT50, consisting of multiple layers of a multi-head attention module and a feed-forward network. LiGhT takes the molecular line graphs as input, which represent the adjacencies between edges of the original molecular graphs 28, Supervised+Attribute Masking (MaskingSup)28, Supervised+Edgepred (EdgepredSup)28, and Supervised+Infomax (InfomaxSup)28 also integrate additional knowledge by introducing supervised graph-level pre-training tasks in their self-supervised learning frameworks, such as predicting the presence of molecular motifs or bio-activities of molecules. Although the self-supervised learning strategies of these methods are not well-defined, they still outperformed other baseline methods that do not incorporate additional knowledge. This observation demonstrated the significance of integrating additional knowledge to enhance the efficacy of pre-training on molecules.We initially compared KPGT to 19 state-of-the-art self-supervised learning-based methods , which are pivotal in the field of drug discovery and development. We conducted a comparison between KPGT and 28 baseline methods from the TDC leaderboards. These baselines included 16 deep learning-based methods and 12 machine learning-based methods 66. In this evaluation, we assessed KPGT against 24 baseline methods from the MoleculeACE benchmarking platform, comprising seven deep learning-based methods and 17 machine learning-based methods  exhibited highly competitive prediction performance, even surpassing the previously designed deep learning-based methods in multiple cases. For the datasets from TDC, machine learning-based methods served as the most competitive baseline methods on 13 out of 22 datasets  for molecules from the CYP3A4 dataset using the pre-trained KPGT. Next, we randomly sampled 200 molecules from the CYP3A4 dataset to form a test set, while the remaining molecules constituted the training set. We then employed k-nearest neighbor classification (kNN) based on the neural fingerprints to make predictions for the molecules in the test set. We compared the performance of KPGT with two classic fingerprints widely used in the field, namely extended-connectivity fingerprints (ECFP)14 and RDKit fingerprint (RDKFP)44, along with two neural fingerprints derived from two state-of-the-art self-supervised learning-based methods, GROVER38 and GraphCL35. Figure After demonstrating the superiority of KPGT in predicting molecular properties, we conducted further investigations to unveil the reasons underlying its remarkable performance. We initiated this exploration by analyzing the latent space constructed by KPGT after pre-training. For this analysis, we employed a dataset comprising measurements of bio-activity  for 12,328 molecules targeting the CYP3A4 enzyme, which plays a crucial role in drug metabolismWe carried out additional tests to further verify our findings. More specifically, we first retrieved the closest data point in the latent space for each molecule from the sampled test set, yielding 200 pairs of molecules. Next, we calculated five descriptors that play important roles in drug discovery, including molecular LogP (MolLogP), molecular weight (MolWt), topological polar surface area (TPSA), number of rotatable bonds (NumRotatableBonds), quantitative estimate of drug-likeness (QED), and synthetic accessibility (SA), for the sampled molecules and queried ones. We then measured the correlation between the pairs of sampled and queried molecules for each descriptor in terms of Spearman’s rank correlation coefficient (Spearman’s r) and Pearson correlation coefficient (Pearson’s r). Figure 70 to visualize the molecular representations within the test set. From the results, KPGT provided a clear separation for the representations of inhibitors and non-inhibitors, indicating that KPGT can learn the distinguishable features of molecules with different properties in the finetuning process 75. The availability of high-quality experimental data for HPK1 and FGFR1 significantly facilitates the development and validation of the AI-based computation model, providing adequate data for evaluating the practicality and prediction performance of KPGT. In this section, we carried out evaluation tests, drug repurposing, and docking analyses for both targets, serving as a proof-of-concept validation of the effectiveness of KPGT in real-world drug discovery scenarios.Hematopoietic progenitor kinase 1 (HPK1) and fibroblast growth factor receptor (FGFR1), which are implicated in a variety of cancer types, have been extensively studied for antitumor therapyTo facilitate the identification of potent inhibitors against HPK1, we collected 4442 molecules with experimentally determined potency against HPK1 from previous patents and research, measured in terms of the negative logarithm of the half maximal inhibitory concentration (pIC50). We comprehensively evaluated the prediction performance of KPGT on this dataset using three distinct splitting approaches, including scaffold splitting, time splitting, and domain transfer (“Methods”). Comparison results between KPGT and 19 self-supervised learning-based baseline methods are detailed in Fig. 76. Then we finetuned KPGT on the pIC50 dataset of HPK1 inhibitors and made predictions for the molecules from the FDA dataset. Supplementary Table 77.We next sought to use KPGT to identify potential HPK1 inhibitors through drug repositioning. More specifically, we first obtained 2718 US Food and Drug Administration-approved (FDA) drugs (denoted as the FDA dataset) collected from DrugBank79, a widely used docking software, was employed for these tests. The reference protein-ligand structure (PDB ID: 7SIU80) guided the identification of the binding pocket. As depicted in Fig. 84, signifying the substantial potential for these molecules as HPK1 inhibitors. Additionally, we conducted an in-depth analysis of the protein-ligand interactions for all the molecules that had not been reported in the literature using a widely applied protein-ligand interaction profiler named PLIP85. Figure 80). These observations showcased that the molecules can tightly bind to HPK1, validating the reliability of the docking results. Supplementary Fig. To strengthen our findings, we further conducted docking analyses for the top 20 predictions from KPGT. Autodock Vina75. We first collected 12,461 existing molecules for FGFR1 from patents and previous studies with experimental pIC50 values. We evaluated the prediction performance of KPGT on the FGFR1 dataset under the scaffold splitting and time splitting settings. Figure 86) was employed as a reference for the binding pocket identification. As shown in Fig. 85, the ligand brigatinib was tightly bound to the protein FGR1. Specifically, it formed four hydrophobic interactions, one hydrogen bond, and one salt bridge with FGFR1 . In Supplementary Fig. Next, as a second prototypical example, we conducted tests on FGFR1, another promising druggable target associated with tumor progression and invasion57), and KPGT-LiGhT+GIN (replacing backbone model LiGhT with GIN45). Additionally, for a fair comparison, we ensured that all models had approximately the same number of parameters (around 3.5 million). Supplementary Table To validate the effectiveness of the specific design choices of KPGT, we conducted comprehensive ablation studies. Specifically, we introduced several modified frameworks based on KPGT with specific restrictions: KPGT-Pretrain (without pre-training), KPGT-KN (without knowledge nodes), KPGT-PE (without path encoding module), KPGT-DE (without distance encoding module), KPGT-LG , KPGT-LiGhT+Graphormer . This discrepancy indicated that the additional knowledge incorporated in KPGT effectively guided the model to predict the masked nodes, thereby enabling it to capture the rich semantics of molecules. Moreover, this observation aligned with the finding in the field of CV that applying larger masking rates in self-supervised learning can yield improved performance on downstream tasks32.We also investigated the effect of different masking rates on the prediction performance of KPGT. We individually pre-trained KPGT with masking rates of 15, 30, 50, and 60%, and subsequently evaluated their prediction performance. From the results, setting the masking rate to 50% achieved the best prediction performance In this study, we develop and establish KPGT, a self-supervised learning framework that offers improved, generalizable, and robust molecular property prediction through significantly enhanced molecular representation learning. By leveraging a high-capacity backbone model called LiGhT, KPGT comprehensively captures the inherent structural information within molecular graphs. More importantly, KPGT introduces a knowledge-guided pre-training strategy that can robustly address the limitations of previously ill-defined pre-training approaches, empowering our model to provide semantic-enriched molecular representations. In addition, KPGT incorporates several finetuning strategies that effectively integrate the acquired knowledge from the pre-trained model, leading to improved performance on downstream molecular property prediction tasks. With these advancements, KPGT achieved great improvement on 63 datasets compared with several baseline methods. Remarkably, KPGT showed practical applications in the identification of multiple potential inhibitors for two antitumor targets.13 can calculate over 1800 molecular descriptors, presenting an opportunity to incorporate a broader range of knowledge to further enrich the representation learning of molecules. Moreover, further studies could encompass the integration of three-dimensional (3D) molecular conformations into the pre-training process, thus enabling the model to capture vital 3D information regarding molecules, and potentially enhancing the representation learning capabilities. Additionally, while KPGT currently employs a backbone model with approximately one hundred million parameters, along with pre-training on two million molecules, exploring larger-scale pre-training could offer even more substantial benefits for molecular representation learning. Overall, we anticipate our proposed method will offer a general self-supervised learning framework for accelerating AI-aided drug discovery.Despite the advantages of KPGT for effective molecular property prediction, there remain a few limitations. First, the integration of additional knowledge is the most distinguishable feature of our proposed method. Except for the 200 molecular descriptors and 512 RDKFP employed in KPGT, there is potential to incorporate various other types of additional informative knowledge. For instance, MordredNv stands for the number of nodes. We initialize the features of nodes and edges in the molecular graph via RDKit44. The atom and bond features used in this work are summarized in Supplementary Tables vi and edge ei,j as Dv and De stand for the dimensions of features of nodes and edges, respectively.Given a SMILES representation of a molecule, we first abstract it as a molecular graph mentclass2pt{minim57, we transform the molecular graph ei,j in For each edge For every two edges in To fully exploit the structural information from molecules, especially the chemical bonds that have been neglected in the previously defined transformer architecturesWe calculate the initial feature embedding 50, which consists of multiple transformer layers. More specifically, given the node feature matrix l first feeds it into a multi-head self-attention module:Hl−1 stands for the node feature matrix at the (l − 1)-th layer, k-th head at layer l, Nh stands for the number of self-attention heads, and Hl is then passed to an FFN:87, GELU(⋅) stands for the GELU activation function88, and l.We then propose Line Graph Transformer (LiGhT) to encode features of the molecular line graph. LiGhT is built upon a classic transformer encoderDirectly applying the above classic transformer architecture can lead to a significant loss of structural information of molecules since it ignores the connectivity of graphs. Therefore, we further employ path encoding and distance encoding modules to introduce the structural information into the multi-head self-attention layer.89, Np stands for the length of the path, n-th node in the shortest path, n-th node in the path, Dp stands for the dimension of the path embedding.For each pair of nodes 57, we also leverage the distances between pairs of nodes to further encode the spatial features of the molecular line graphs. More specifically, given nodes di,j stands for the derived distance between Dd stands for the dimension of the distance embedding.Following refs. Ap and Ad are the path encoding matrix and the distance encoding matrix, respectively.Then, to introduce the encoded structural information into the model, we rewrite the formula of the attention matrix entclass1pt{minimaHere, we discuss the main advantages of our proposed model compared with the previously defined graph transformers:57. In our case, LiGhT fills this gap and fully exploits the intrinsic features of chemical bonds.First, by representing molecular graphs as line graphs, LiGhT emphasizes the importance of chemical bonds in molecules. Chemical bonds are the lasting attractions between atoms, which can be categorized into various types according to the ways they hold atoms together, resulting in different properties of the formed molecules. However, the previously defined transformer architectures either omit the edge features or only introduce chemical bonds as the bias in the self-attention module, ignoring the rich information from chemical bonds57 when encoding the paths, they only consider the edge features and ignore the node features in the paths. On the other hand, our path encoding strategy incorporates the features of the complete paths between pairs of nodes, thus encoding the structural information more precisely compared to the previous methods.Second, although strategies like path encoding have already been proposed in previous graph transformer architecturesIn summary, LiGhT provides a reliable backbone network for accurately modeling the structural and semantic information of molecular line graphs.44. Additionally, knowledge can encompass the experimentally measured characteristics of molecules, such as the comprehensive information on the bio-activities of 456,000 molecules across 1310 bioassays in the preprocessed ChEMBL dataset90. We employed 200 molecular descriptors and 512 RDKit fingerprints in our proposed method, which can be readily generated using RDKit44, a widely used cheminformatics tool. The complete list of these molecular descriptors and examples of RDKit fingerprints can be found in Supplementary Table In this study, we define knowledge as any quantifiable information that characterizes the features of molecules. This includes various types of information, such as molecular descriptors and fingerprints that are easily accessible through current cheminformatics toolsOur pre-training strategy is based on a generative self-supervised learning scheme, which first randomly selects a proportion of nodes in graphs. Then for each selected node, it is replaced with a mask token, a random node or the unchanged node with a ratio of 8:1:1. Finally, the model learns to predict the type of the original node with a cross-entropy loss. In the pre-training, we also randomly mask a proportion of the initial features of K nodes and learn to predict the masked molecular descriptors and fingerprints. The prediction of the masked molecular descriptor is formulated as a regression task equipped with an RMSE loss, while the prediction of fingerprint is formulated as a binary classification task equipped with a cross-entropy loss.61, re-initialization (ReInit)61, FLAG62 and, L2-SP63. LLRD and ReInit are proposed mainly based on the fact that different layers of a model capture different kinds of information, where the bottom layers tend to encode the information more general to the downstream tasks while the top layers tend to encode information related to the pre-training tasks. More specifically, LLRD implements the discriminative learning rates for different layers of a model. This is achieved by setting an initial learning rate of the top layer and using a multiplicative decay rate to decrease the initial learning rate layer-by-layer from top to bottom. ReInit re-initializes the parameters of the top-n layers of a model before finetuning. FLAG is a data augmentation method that iteratively augments the node features by injecting the gradient-based adversarial perturbations during finetuning. L2-SP proposes a regularization scheme to explicitly promote the similarity of the finetuned model with the initial one in the finetuning process.To fully take advantage of the abundant knowledge captured in the pre-training stage, KPGT introduces four finetuning strategies, including layer-wise learning rate decay (LLRD)91 version 1.10.0 and DGL92 version 0.7.2 with CUDA version 11.3 and Python 3.7. We implemented a 12-layer LiGhT as the backbone network with 768 hidden units and 12 self-attention heads. A mean pooling operation that averaged all the nodes in individual graphs was applied on top of the model to extract the molecular feature representations. An Adam optimizer with weight decay 1e−6 and learning rate 2e−4 was used to optimize the model. The model was trained with a batch size of 1024 for a total of 100,000 steps. The KPGT had around 100 million parameters. We set the masking rate of both nodes and additional knowledge to 0.5. The pre-training of KPGT took about two days on four Nvidia A100 GPUs. More detailed configurations of KPGT in the pre-training and finetuning processes are summarized in Supplementary Table All models were implemented in PyTorch38, a scaffold splitting scheme was utilized to partition each dataset into training, validation, and test sets with a ratio of 8:1:1. This splitting scheme ensured the molecules in the test sets differed structurally from those in the training set, offering an ideal scenario to evaluate the robustness and generalizability of prediction models. We utilized the area under the receiver operating characteristic curve (AUROC) to evaluate classification tasks, while for regression tasks, we employed the root-mean-square error (RMSE) as the evaluation metric. The results were reported based on three independent runs with different random seeds.In our first benchmarking test in the “Results” section, we employed 11 molecular property datasets, including eight classification datasets and three regression datasets. Detailed information of these datasets is available in Supplementary Note The TDC benchmarking platform provides 22 molecular property prediction datasets. These datasets span a broad spectrum of molecular properties, including absorption, distribution, metabolism, excretion, and toxicity (ADMET). We strictly adhered to the evaluation protocols provided by the original TDC benchmarking platform. Each dataset was split into training, validation, and test sets with a ratio of 7:1:2 using a scaffold splitting scheme. The mean absolute error (MAE) or Spearman’s rank correlation coefficient (Spearman’s r) was employed to evaluate the prediction performance of KPGT on regression tasks, while AUROC or the area under the precision-recall curve (AUPRC) was used to evaluate the prediction performance of KPGT on classification tasks For the tests on the datasets for HPK1, we evaluated the prediction performance of KPGT on this dataset in three distinct scenarios: (1) scaffold splitting, i.e., splitting the dataset as training, validation, and test sets with a ratio of 8:1:1 using scaffold splitting; (2) time splitting, i.e., splitting the dataset according to the time that the corresponding patents were published (the molecules from the patents published before 2021 were used as training while those molecules published after 2021 were used as testing); and (3) domain transfer, i.e., collecting 1615 molecules with the negative logarithm of inhibitory constants (pKi) against HPK1 as an independent test set. We excluded the molecules that overlapped with the pre-training data employed by KPGT in this test. Spearman’s r and Pearson correlation coefficient (Pearson’s r) were employed as the evaluation metrics. The results were reported based on three independent runs with different random seeds.For the tests on the datasets for FGFR1, we evaluated the prediction performance of KPGT in the scaffold splitting and time splitting scenarios. We excluded the molecules that overlapped with the pre-training data employed by KPGT in this test. Spearman’s r and Pearson’s r were employed as the evaluation metrics. The results were reported based on three independent runs with different random seeds.Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data