Developing Nomogram and Evaluating Prognostic Factors of Limb Osteosarcoma Patients: A Retrospective Study

Introduction

Osteosarcoma is a primary bone malignant tumor which is most common in children and adolescents, followed by the elderly [1]. Osteosarcoma is the most common types of primary malignant bone tumors, although malignant bone tumors have a lower incidence compared with other types of tumors [2]. The most common primary site of osteosarcoma is the metaphysis of long bones, especially the proximal tibia and distal femur, followed by the proximal humerus. It rarely appears in other parts of the body. Most patients with osteosarcoma initially show a single lesion that is highly occult, therefore it is easy to be confused with trauma or growth-pain [3]. Until the 1940s when the first resection for osteosarcoma was performed and chemotherapy was used, amputation was the main treatment intervention, which had a positive impact on the patient’s prognosis [4]. With the development of radiotherapy and chemotherapy, the survival time of osteosarcoma patients has been prolonged. Afterwards, limb salvage surgery for osteosarcoma combined with radiotherapy and chemotherapy significantly made patients have a good prognosis. However, although many patients have received surgery combined with radiotherapy and chemotherapy, they still have recurrence and metastasis. Therefore, it is very important to understand the influencing factors of the development, prognosis, and occurrence of osteosarcoma.

Although the survival rate of patients with osteosarcoma has improved in recent years, it is still very important to accurately predict the survival of patients and determine independent risk factors. Nomogram is a useful tool to solve the above problems. A nomogram is an effective prognostic tool. Hopefully, a nomogram can visually and effectively display the results of statistical analysis through images. The predictive ability and accuracy of nomogram have been widely proven in different types of cancer research. Many studies have proved that the survival time of patients with osteosarcoma of extremities is completely different from that of patients with osteosarcoma of axial bone [5,6]. To our knowledge, No researchers have found an accurate nomogram to predict the survival time of patients with limb osteosarcoma The Surveillance, Epidemiology, and End Results (SEER) database was established in 1973 and collected data from 18 cancer registries. This database covers 28% of the US population. By using this database, We collected a nationwide population-based cohort to answer the following questions:

(1) Which clinical characteristics can independently affect the survival of patients with limb osteosarcoma?

(2) Can we construct the corresponding nomogram to accurately predict the 3-year, 5-year and 10-year OS and CSS of individual limb osteosarcoma patients?

Previous studies have shown that recurrence, tumor size, metastasis and response to chemotherapy are the main prognostic factors of osteosarcoma, but these factors are only used as a single indicator for evaluating prognosis, thereby limiting their ability to accurately predict individualized survival in patients with osteosarcoma [7-9]. Considering the limitations of a single factor, we tried to develop a new prognostic model. In this study, we established a nomogram, which is an effective prognostic tool that can integrate all the prognostic factors of patients and more accurately estimate the prognosis of each patient. A nomogram is a visual expression of Cox multivariate analysis, which can be used to estimate the survival probability of a patient [10]. Because osteosarcoma has the highest incidence of all malignant bone tumors, and the most common site of incidence is the limbs. Therefore, the purpose of this study was to identify the independent prognostic factors that affect the survival of patients with osteosarcoma of the extremities and to predict the prognosis with the predictive ability of nomograms. The SEER database was widely used in clinical research of various cancers including osteosarcoma. We used the SEER database to collect sufficient clinical data for the research. The SEER database contains data on various cancers. More importantly, the database has relatively complete and updated follow-up information. We collected the clinical characteristics of patients with osteosarcoma of the limbs from the SEER database from 2004 to 2015 and analyzed the data to construct an accurate nomogram to predict the prognosis of osteosarcoma.

Methods

Data Selection

The SEER database was created by the National Cancer Institute, USA and includes 18 cancer registries, covering approximately 28% of the US population. After the approval of our registered account, we could use the patient data in the SEER database for research. This study complied with all guidelines of the “Declaration of Helsinki” on ethical considerations in human trials. The data published in the SEER database does not require any patient informed consent. We collected patient age, gender, race, diagnosis year, Tumor, Node, Metastasis (TNM) stage, grade, tumor size, treatment intervention (surgery, radiotherapy, chemotherapy), the extent of disease(EOD), survival time, cause of death, and other clinical characteristics. The inclusion criteria of this study were:

(1) The primary site is limited to limbs,

(2) Diagnosed between 2004 to 2015,

(3) The primary malignant tumor is osteosarcoma,

(4) Complete follow-up,

(5) Known survival months and death reasons after diagnosis.

The exclusion criteria were:

(1) Unknown race,

(2) Use of unknown surgery, radiotherapy, and chemotherapy,

(3) Unknown AJCC/TNM staging, Tx(Primary tumor cannot be assessed), Nx(Regional lymph nodes cannot be assessed), Mx(Distal metastasis cannot be assessed),

(4) Unknown tumor grade, size, and EOD.

Age was divided into 3 groups: 1-39, 40-59, >59 years. The races were categorized as black, white, or other (Alaska Native / American Indian, Asian/Pacific Islander). The diagnosis durations of the screened patients were divided into two groups: 2004-2009 and 2010-2015. Tumors were graded into two types; low grade (gradeⅠ, gradeⅡ) and high grade (gradeⅢ, gradeⅣ). Responses on surgery, radiotherapy, and chemotherapy were divided into yes or no. Otherwise, tumor sizes were categorized into 3 groups: 1-50 mm, 51-99 mm, and >99 mm. AJCC osteosarcoma staging system has divided the extent of disease (EOD) into three types [9]: Localized (tumor confined to the periosteum), regional (tumor extended beyond the periosteum without distant metastasis), and distant (having the metastatic disease at presentation).In this study, 59 patients had distant metastasis. The number of these patients was small. So these patients were included in the regional group. Finally, the extent of tumor disease was divided into two groups: localized and regional/distant.

Statistical Analysis

According to the above inclusion and exclusion criteria, we collected 1383 samples of limb osteosarcoma and randomly divided them into two cohort in the ratio of 7:3 to construct and verify the nomogram (Figure 1). Training cohort had 968 samples and validation cohort had 415 samples. The chi-square test was employed to compare the clinicopathological characteristics of patients between the training group and the verification group. A univariate Cox analysis was employed in the training cohort to determine different prognostic factors (e.g., age, race, sex, tumor size, year of diagnosis, grade, AJCC/TNM stage, surgery, radiotherapy, chemotherapy, EOD) included in the current study. Then the possible prognostic factors in univariate analysis were included in the multivariate Cox analysis (p<0.05) to determine independent prognostic factors (p<0.05). We also examined the hazard ratio of each variable and used the independent prognostic factors selected in the multivariate Cox analysis to construct a nomogram showing 3-, 5-, and 10-year OS and CSS. OS indicates the time from the date of diagnosis to death due to any cause. CSS is defined as the time from the diagnosis of the tumor to the death of the tumor. We evaluated the predictive ability of the nomogram through the calibration curve and the consistency index (C-index). The C-index ranges from 0.5 to 1.0 (0.5 means complete disagreement; 1.0 means complete agreement, c-index >0.7 means that the model has good accuracy).

We established a calibration curve to determine the consistency of the predicted survival period with the actual survival period. The above statistical analyses were performed by using statistical software SPSS, version 25.0 (IBM Inc, USA) and R software (version 4.0.2) (Figure 1).

Figure 1: Flow chart.

Results

Clinical Data of the Studied Cases

The clinicopathological characteristics of included patients are shown in (Table 1). Among the 1383 patients with limb osteosarcoma, 1126 (81.4%) were younger than 40 years old, 159 (11.5%) were 40-59 years old, and 98 (7.1%) patients were older than 59 years. Tumors tend to occur more in young people. In terms of sex, 772 (55.8%) were male and 611 (44.2%) were female. In terms of race, Whites showed a higher proportion of patients (74.8%). Osteosarcoma usually had a higher tumor grade. There are 152 cases (11.0%) of low-grade (grade Ⅰ, grade Ⅱ) osteosarcoma, and 1231 cases (89.0%) of high-grade (grade Ⅲ, grade Ⅳ) osteosarcoma, and they tended to occur in the lower limbs (1162, 84.0%). Since the number of cases of distant lesions was small (59), we clubbed distant lesions and regional lesions into one group, including localized lesions (466) cases (33.7%), regional lesions/distant lesions (917) cases (66.3%). Most of the patients received surgery (n=1316, 95.2%) and chemotherapy (n=1193, 86.3%), and fewer patients received radiotherapy (n=53, 3.7%). At the last follow-up, 448 (32.4%) patients died, out of them 389 cases (28.1%) died of osteosarcoma. There were no significant statistic difference observed between the training group and the validation group in terms of patient’s race, sex, age, primary site, tumor size, year of diagnosis, EOD, surgery, grade, AJCC/TNM stage, radiotherapy and chemotherapy.

Table 1: Clinical and pathological features of the study population.

Prognostic Factors Affecting OS and CSS

The prognostic factors affecting the OS and CSS in the patient data were screened through univariate Cox analysis and multivariate Cox analysis. We observed that nine factors including age, grade, surgery, T stage, N stage, M stage, radiotherapy, tumor size, and EOD were significantly related to OS and CSS (p<0.05). There was no statistically significant correlation observed between gender, race, chemotherapy, primary site, and year of diagnosis. Furthermore, these nine factors were selected for multivariate Cox analysis to control different confounders. The results of multivariate Cox analysis showed that six factors including age, grade, surgery, tumor size, M stage and EOD were independent prognostic factors for OS (Table 2) and CSS (p<0.05)(Table 3). We also used these factors to create a survival curve between OS (Figure 2) and CSS (Figure 3).

Figure 2: Kaplan-Meier (K-M) analysis for OS of patients with limb osteosarcoma about A. Age, B. EOD, C. Grade, D. AJCC_M, E. Size, F. Surgery.

Figure 3: Kaplan-Meier (K-M) analysis for CSS of patients with limb osteosarcoma about A. Age, B. EOD, C. Grade, D. AJCC_M, E. Size, F. Surgery

Table 2: Univariate and Multivariate Analyses of overall survival in the Training cohort.

Table 3: Univariate and multivariate analyses of cancer-specific survival in the training cohort.

Construction and Validation Of OS And CSS Nomogram

Based on the results of the Cox analysis, we included six important independent factors: age, grade, surgery, M stage, tumor size, and EOD into the prognostic nomogram in order to estimate 3-, 5-, and 10-year OS and CSS in patients with limb osteosarcoma (Figure 4). The nomogram gives score of every prognostic variable on the point scale (Table 4). The following steps are involved in development of the nomogram: 1. Based on individual limb osteosarcoma patient’s prognostic factors, scores related to each prognostic factor can be obtained, 2. Add all the points to get the “total point”, 3. Draw a vertical line from the “total point” column to the survival probability column to get the corresponding survival rate. (Figure 4). As an example, a patient diagnosed with primary limb osteosarcoma was aged 38 at the time of diagnosis with a high grade, tumor size of 80 mm M stage was M0. This patient was presented with regional disease and underwent surgery. According to our nomogram, the patient’s OS and CSS scores were 9.3 and 9.2 points, respectively.

Figure 4: Nomograms to predict 3-,5- and 10-year overall survival (A) and cancer-specific survival (B) for osteosarcoma patients.

Table 4: Detailed points of each predictor in the nomograms.

The 3-year OS and CSS scores of the patient were 0.86 and 0.89, respectively. The 5-year OS and CSS rates were 0.79 and 0.84, respectively. The 10-year OS and CSS rates were 0.74 and 0.81, respectively. The nomogram was verified both internally and externally. For OS, the C-indexes of the training group is 0.744(95%CI: 0.714-0.774) and the C-indexes of the validation group is 0.736(95%CI: 0.694-0.778). For CSS, the C-index of the training group and the validation group were 0.746(95%CI: 0.715-0.777) and 0.737(95%CI: 0.692-0.782), respectively. The internal and external calibration curves of 3-, 5-, and 10-year OS and CSS showed strong consistency of the predicted results of the nomogram with the actual results (Figures 5 & 6).

Note: The cohort was divided into five subgroups with the equal sample size. The dashed line represents an excellent match between actual survival outcome (Y-axis) and nomogram prediction (X-axis). Closer distances between dashed line and points indicated higher prediction accuracy

Figure 5: Internal calibration plots of 3-year

A. 5-year

B. and 10-year

C. overall survival nomogram calibration curves; 3-year

D. 5-year

E. and 10-year

F. cancer-specific survival nomogram calibration curves.

Note: The cohort was divided into five subgroups with the equal sample size. The dashed line represents an excellent match between actual survival outcome (Y-axis) and nomogram prediction (X-axis). Closer distances between dashed line and points indicated higher prediction accuracy.

Figure 6: External calibration plots of 3-year

A. 5-year

B. and 10-year

C. overall survival nomogram calibration curves; 3-year

D. 5-year

E. and 10-year

F. cancer-specific survival nomogram calibration curves.

Discussion

The survival of limb osteosarcoma patient is determined by many factors. Several researchers have conducted studies on the prognostic factors of osteosarcoma, including primary tumor size, chemotherapeutic efficacy, recurrence, and metastasis [6,7,11]. However, it is inappropriate to evaluate the prognosis of osteosarcoma patients using a single variable. A nomogram can intuitively and effectively evaluate prognosis based on a multivariate regression model [12]. It can provide a graphical calculation scale method for estimating the probability of overall patient survival [10]. Nomogram has been widely used in the survival prediction of individual patients in recent years [13-21], and can comprehensively incorporate independent prognostic factors and predict the survival rate of 3-, 5-, and 10- years. Osteosarcoma is the most common type of primary malignant bone tumor [19,20]. Osteosarcoma usually occurs in the limb extremities [21]. However, to our knowledge, so far researchers have not constructed any nomogram for the prognosis of extremity osteosarcomas. Several studies have established that the survival prognosis of patients with limb osteosarcoma is completely different from that of patients with primary osteosarcoma of the axial bone [5,6]. By using ample data in the SEER database, we constructed a comprehensive nomogram to predict the 3-, 5-, 10-year OS and CSS of patients with limb osteosarcoma.

We determined the independent prognostic factors of the OS and CSS of extremity osteosarcoma patients based on the screening data in the current study, through univariate and multivariate Cox analysis. These factors include M stage, grade, surgery, age, tumor size, and EOD. Previous studies have shown that age has great effect on the prognosis of osteosarcoma patients. [21-23]. One study has reported a bad prognosis in patients with osteosarcoma over the age of 40 years [24]. Our study also highlighted that that age is the most significant factor affecting the survival of patients with limb osteosarcoma. High-grade (poorly differentiated or undifferentiated) tumors are more likely to recur and metastasize than low-grade (well-differentiated or moderately differentiated) tumors [25]. Also, a larger tumor indicates a poor prognosis for osteosarcoma patients [26-28]. One study has reported that patients with larger tumor size are more likely to have metastases [29]. Surgery can also remarkably affect the prognosis of patients. One study showed a positive impact of surgery on prognosis and survival of patients with osteosarcoma [30]. These findings were validated in our study also showed that patients with low-grade tumors or smaller primary tumors or surgical treatment have a better prognosis. In the AJCC/TNM stage, M stands for distant metastasis. Our study showed a bad prognosis in limb osteosarcoma patients with distant metastasis. In terms of EOD, the metastatic disease has long been considered as an independent risk factor for higher mortality [7,31,32]. Our research indicates that the prognosis of patients with tumors in regional stage and distant metastasis stage is worse than that of patients with tumors in the local stage.

Limitations

There are some limitations to this study. First, patients diagnosed only after 2004 had information on AJCC/TNM stage in the database, so we excluded cases diagnosed before 2004 from the study. Second, there may be some important independent prognostic factors such as chemotherapy regimens, the status of tumor margins, and whether there is nerve/vascular invasion that might not have been identified by researchers or recorded in the SEER database [33-35]. These factors may be related with the prognosis of limb osteosarcoma patients, but were not included in this study. We only considered 3-, 5-, and 10-year survival as the study endpoint. However, maybe recurrence at a certain time point can be assessed as an outcome [36]

Conclusion

Taken together, based on the large database, we analyzed the factors affecting limb osteosarcoma. Also, we determined that tumor size, age, M stage, grade, surgery and EOD are the independent prognostic factors of OS and CSS in patients with limb osteosarcoma. We also developed and validated the nomogram to evaluate the OS and CSS of limb osteosarcoma patients at 3-, 5- and 10- years. Nomogram has high accuracy and applicability. Hopefully, our nomogram could be used as a convenient and effective tool. It can help doctors conduct personalized survival assessments and screen outpatients with high mortality rates, to provide a reference for subsequent treatment options and follow-up strategies.

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Role of Umbilical Cord Abnormalities in Pathogenesis of Acute Intrapartum Fetal Asphyxia and Perinatal Mortality

Introduction

Despite the constant improvement of medical technologies and an increase in the quality of obstetric and perinatal care, acute intrapartum fetal asphyxia and stillbirth continue to remain urgent problems of modern medicine [1,2]. Therefore, the timely diagnosis and correction of potentially preventable causes of stillbirth and perinatal mortality is of particular relevance today. Such causes include umbilical cord abnormalities, which, according to various scientific data, account for about 10% of the possible or probable causes of stillbirth, and are more common after 32 weeks of pregnancy [3-7]. According to R. Bukowski (2017) and H. Mantakas (2018) et al, the contribution of umbilical cord abnormalities to stillbirth can vary widely, and range from 8% to 65% [8,9]. The human umbilical cord is a multi differentiated, constantly growing, extraembryonic organ that ensures the connection of the fetus with the placenta and its life support in the dynamics of pregnancy and childbirth [3-5]. Umbilical cord abnormalities, that can cause acute intrapartum fetal asphyxia include: entanglement around fetus neck and body parts [8-11]; umbilical cord prolapse [3-5]; true nodes, torsion, or strictures with blood clots [3,5,8]; vessels previa [12-15]; marginal or membrane attachment [3-5,16]; excessive or insufficient number of coils, pathology of Wharton’s jelly, vessels and umbilical cord length [3-5,17-22].

According to J.E. Lawn et al (2016), stillbirth is currently not declining, and continues to increase at an accelerated rate by 2030 [23]. Therefore, prenatal diagnosis of umbilical cord abnormalities is an urgent task of modern obstetrics in the 21st century. According to many researchers, the role of umbilical cord abnormalities as a cause of intrapartum fetal asphyxia is insufficiently understood [3- 5,24,25], which determined the purpose of this study. The aim of the study was to identify the role of umbilical cord abnormalities in the development of acute intrapartum fetal asphyxia and perinatal mortality in singleton term delivery in cephalic presentation.

Materials and Methods

A systemic structural analysis was made of 200 cases of acute intrapartum fetal asphyxia in the Ryazan region in 2011–2020. The analysis included every first 20 cases of this pathology in each year. The study inclusion and non-inclusion criteria were clearly defined (Table 1). The main study inclusion criteria: singleton delivery during full-term pregnancy; fetus cephalic presentation; normal size of the fetus and mother’s pelvis; the presence of normal indicators of non-stress test and (or) Doppler measurements of fetal hemodynamics, and (or) biophysical profile of the fetus before delivery. The main study non-inclusion criteria: delivery with multiple pregnancy; premature or late delivery; large fetus; anatomically narrow mother’s pelvis; placenta previa; absence of normal indicators of non-stress test and (or) Doppler measurements of fetal hemodynamics, and (or) biophysical profile of the fetus before delivery. A positive non-stress test and (or) normal Doppler parameters of the fetal hemodynamics and (or) the biophysical profile of the fetus before delivery were an indirect confirmation of the development of acute intranatal fetal asphyxia.

Table 1: The study inclusion and non-inclusion criteria.

The work used methods of expert evaluation of clinical, laboratory, instrumental and special methods of research, tactics planning and management of delivery, as well as a systemstructural analysis of the causes of acute intrapartum fetal asphyxia. The source of information was the primary medical documentation - an individual card of the pregnant woman and the puerperal, the historys of delivery and newborns, protocols for pathoanatomical and histological studies of the placenta and fetus (in case of stillbirth and early neonatal death). An expert evaluation of the protocols of delivery, the results of ultrasound examination and the biophysical profile of the fetus (if any), the protocols of pathoanatomical and histological examination of the placenta, as well as the fetus (in case of stillbirth and early neonatal death) was completed. The biophysical profile of the fetus was assessed by five indicators from 0 to 2 points each: non-stress test, physical activity, respiratory movements and muscle tone of the fetus, the amount of amniotic fluid. The criterion for the normal state of the fetus was a score of 8–10 points. The condition of the newborns was assessed in a comprehensive manner - according to the results of pH-metry of umbilical cord blood (if the study was timely) and according to the Apgar scale at 1 and 5 minutes of life. Criteria for severe fetal asphyxia - pH below 7.2, Apgar score from 3 to 0 points. Criteria for moderate fetal asphyxia - pH from 7.20 to 7.25, Apgar score from 7 to 4 points. Criteria for the normal state of the fetus - pH above 7.25, Apgar score 8-10 points. Statistical processing of the results was carried out using the Statistica v. 11 (StatSoft, Inc., USA) using parametric and nonparametric statistics methods

Results and Its Discussion

The structure of the main identified causes of acute intranatal fetal asphyxia shown in the (Figure1). The first ranking place in the structure of causes of acute intrapartum fetal asphyxia was taken by the use of oxytocin in labor – 35.5%, the second – premature detachment of the normally located placenta – 31% (рχ2 <0.05). Umbilical cord abnormalities ranked third in the structure of the causes of acute intrapartum fetal asphyxia (23.5%) and were found 1.3-1.5 times significantly less frequently than the previous causes (рχ2 <0.05). Umbilical cord prolapse and labor activity discoordination were significantly less frequent than other causes of intrapartum fetal asphyxia, and amounted to 5.5% and 4.5%, respectively (рχ2 <0.05). Methods of emergency delivery were used in all cases of acute intranatal fetal asphyxia, in the first stage of labor - abdominal delivery in 111 (55.5%) cases, in the second stage of labor - obstetric forceps in 53 (26.5%) and vacuum extraction of the fetus in 36 (18%) cases.

Figure 1: The structure of the acute intrapartum fetal asphyxia. Statistically significant differences with the proportion of umbilical cord abnormalities according to the χ2 fit criterion (р<0.05): ** – statistically significant higher, * – statistically significantly lower.

Delivery outcomes are presented in Table 2. The largest proportion of newborns in a state of severe asphyxia registered with abnormalities of the umbilical cord – 40.4%. With premature detachment of a normally located placenta, it was 1.5 times less (27.4%, рχ2<0.05), and with labor activity discoordination – it was 1.8 times less (22.2%, рχ2 <0.05). The highest specific weight of perinatal losses was registered with the loss of the umbilical cord loops (18.2%), with abnormalities of the umbilical cord, it was 10.6%, and with premature detachment of a normally located placenta - 8%, which is significantly higher than in the general structure of causes (pχ2<0.05). There were no cases of perinatal death due to incoordination of labor. Some researchers consider umbilical cord prolapse in the general structure of cord abnormalities [3,4]. In our study, out of 11 cases of umbilical cord prolapse, 3 cases (27.3%) had a long umbilical cord (more than 70 cm), 5 cases (45.5%) - polyhydramnios, and 2 cases (18.2%) - a combination of these pregnancy complications.

Table 2: Delivery outcomes in acute intrapartum fetal asphyxia.

Note: n (%) – the absolute number of cases and their proportion for each of the reasons; IFD – intrapartum fetal death; END – early neonatal death; * – statistically significant differences with general causes according to the criterion of matching χ2 (рχ2 <0,05).

Thus, in 7 cases (63.6%) there were no other abnormalities of the umbilical cord during it prolapse. In 6 cases (54.6%), prolapse of the umbilical cord occurred either during amniotomy (2 cases) or shortly after amniotomy (4 cases), which did not allow us to exclude the iatrogenic cause of this pathology. Possible iatrogenic causes of cord prolapse may be incorrect amniotomy and removal of amniotic fluid, especially in polyhydramnios [5]. Therefore, isolated prolapse of the umbilical cord, without combination with other abnormalities of the umbilical cord, we considered as an independent cause of acute intranatal fetal asphyxia, separately from other abnormalities of the umbilical cord. The results of the analysis of the structure of umbilical cord abnormalities in acute intranatal fetal asphyxia are presented in Table 3. The most common probable causes of acute intrapartum fetal asphyxia were a long umbilical cord and an umbilical cord entanglement around fetus neck and (or) body parts (10.5% and 9.5%, respectively, pχ2<0.05). Somewhat less often, acute asphyxia was recorded with the marginal attachment of the umbilical cord (8.5%), pathology of Wharton’s jelly (7.5%) and umbilical cord vessels (6.5%), an excessive number of umbilical cord coils (6.5%). A short umbilical cord and sheathing of the umbilical cord (3.5% each, pχ2<0.05), as well as an insufficient number of cord coils (2.5%, respectively, pχ2<0.05), were significantly less likely among the probable causes (pχ2>0.05).

Table 3: The structure of umbilical cord abnormalities in acute intrapartum fetal asphyxia.

Note: n (%) – absolute number of cases and their proportion; * - statistically significant differences between the proportion of single and multiple umbilical cord abnormalities according to the χ2 criterion (p<0.05).

It is possible that such a rating of probable causes of acute fetal asphyxia among umbilical cord abnormalities is due to their prevalence. Multiple abnormalities of the umbilical cord accounted for 34 (72.3%) cases and occurred 2.6 times significantly more often than single ones (13 (27.7%), px2<0.05). The number of multiple abnormalities in one umbilical cord ranged from 2 to 5, on average 3.1 ± 0.11. Many researchers also point to the predominance of multiple umbilical cord abnormalities over single ones [3- 6,8,10,17]. In our sample, an insufficient number of umbilical cord coils and a deficiency of Wharton’s jelly were always combined with each other (100%), as well as with the sheath or marginal attachment of the umbilical cord. The frequent combination of these abnormalities of the umbilical cord is noted by many authors, who conclude that there is an extremely high risk of acute intrapartum asphyxia of the fetus with a low location of the placenta along the posterior wall of the uterus [3,4,16].

In all 8 cases of intranatal losses, the pathology of Wharton’s jelly was revealed, in 4 of them - its deficiency in combination with an insufficient number of umbilical cord coils and its entanglement. In three cases, the combination of marginal or sheath attachment of the umbilical cord, insufficient number of coils, deficiency of Wharton’s jelly and low location of the placenta on the posterior wall of the uterus turned out to be fatal. Of all the existing pathologies, only the low location of the placenta was diagnosed on ultrasound before delivery. However, it can be assumed that the marginal and sheath attachment of the umbilical cord was localized along the lower edge of the placenta. With such localization, the insertion of the head instantly blocked the umbilical blood flow, which led to acute intrapartum asphyxia and fetal death. The inevitability of intranatal asphyxia of the fetus when a pathologically attached umbilical cord between the head and the sacrum is compressed is indicated in their works by J.H. Collins (2014), I. A. Hammad et al. (2020), M. Arizawa (2021) [3,4,16]. In one case, the fatal combination was the twisting of the umbilical cord around the body of the fetus, an excessive number of spirals, a false node of the umbilical vein, and edema of Wharton’s jelly. Of all the combined pathology, ultrasound diagnosed only a false umbilical vein node. Many authors point to obstructed blood flow with an excess of umbilical cord coils and its rapid decompensation with the addition of additional complications [17-22]. Of all 164 umbilical cord abnormalities registered during histopathological examination, only 29 (17.7%) were diagnosed with ultrasound. The pathology of the number of umbilical cord vessels was always detected. The umbilical cord entanglement around fetus neck and (or) body parts was partly diagnosed. Sometimes the marginal and meningeal attachment of the umbilical cord, false nodes of the umbilical vein were diagnosed. Not diagnosed before delivery - pathology of the length of the umbilical cord and the number of coils of the umbilical cord, aneurysm of the umbilical artery, pathology of Wharton’s jelly.

Conclusion

The Results of the Study lead to the following Conclusions

1. Umbilical cord abnormalities accounted for 23.5% in the structure of probable causes of acute intrapartum asphyxia of the fetus and took third place after the use of oxytocin in childbirth and premature detachment of a normally located placenta (35.5% and 31% respectively, pχ2<0.05).

2. The highest specific gravity of severe intrapartum fetal asphyxia was found in cases of umbilical cord abnormalities (40.4%, pχ2<0.05). The largest share of perinatal losses was recorded in cases of umbilical cord prolapse (18.2%), umbilical cord abnormalities (10.6%) and premature detachment of a normally located placenta (8%), in comparison with other possible causes (pχ2<0.05).

3. Out of 11 cases of umbilical cord prolapse in 7 (63.6%) cases, other abnormalities of the umbilical cord were absent, in 6 (54.6%) - it was impossible to exclude an iatrogenic cause, which made it possible to consider this pathology as an independent cause of acute intrapartum fetal asphyxia, separately from umbilical cord anomalies.

4. Multiple umbilical cord abnormalities were detected 2.6 times significantly more often than single ones (72.3%, pχ2<0.05) during postmortem examination. All perinatal deaths from umbilical cord abnormalities were associated with multiple anomalies.

5. Prenatal diagnosis of umbilical cord abnormalities using ultrasound was 17.7%. Increasing the efficiency of prenatal diagnosis of umbilical cord abnormalities will make it possible to correctly determine the obstetric tactics of delivery, which will help reduce the likelihood of developing acute intranatal fetal asphyxia, severe neonatal asphyxia, stillbirth and perinatal mortality.

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Screening of SARS-CoV-2 Nucleocapsid (N) Protein Inhibitors as Potential Drugs for Sars-Cov-2

Introduction

The current severe acute respiratory syndrome pandemic caused by SARS-COV-2 infection is a serious global health concern due to its rapid spread over with high mortality [1]. According to WHO, globally COVID-19 has spread to more than 224 countries with 273,538,415 million confirmed cases and 5,356,949 million deaths till 17th December, 2021 [2]. COVID-19 spreads through droplets, aerosols, feces, and oral mucus membranes of infected persons. The main symptoms are typical acute respiratory infection, cough, fever, myalgia, pharyngeal erythema, sneezing, acute kidney injury, fatigue, acute liver injury, diarrhea, sore throat, breathing impairment, acute renal failure, encephalopathy, irregular coagulation, and vomiting [3,4]. Based on serology and heritability, corona viruses are classified into four types: α, β, γ, and δ [5]. While α and β corona viruses predominantly infect mammals whereas, γ and δ corona viruses primarily infect birds. So far, seven different varieties of human corona viruses were documented including HCoV-NL63 and HCoV-229E that belongs to α corona virus, and HCoV-OC43, HCoVHKU1, SARS-CoV, MERS-CoV, SARS-CoV-2 to the β corona virus [1].

SARS-CoV-2 belongs to the β coronavirus type, with a singlestranded RNA genome similar to MERS-CoV and SARS-CoV. The new βCoV exhibits 88% sequence similarity with two bat-derived SARSlike coronaviruses. The first open reading frame (ORF) represents roughly 67% of the entire genome encoding 16 non-structural proteins. Coronaviruses have numerous structural proteins that are highly identical across their host species including the small envelope proteins (E), the trimeric spike protein (S), matrix glycoprotein (M) and Nucleocapsid (N) protein in the interior of the virus [6]. A few coronaviruses contain a third membrane-bound glycoprotein, HE (hemagglutinin-esterase). The Nucleocapsid (N) protein comprises a copious part of structural proteins in corona viruses. During replication and transcription, it provides vital information for helical ribonucleoproteins in the infected cells during viral RNA synthesis. Furthermore, the Nucleocapsid (N) protein contains three domains: an RNA-binding domain at the N-terminal region, an essentially tangled central SER/ARG-rich linker that might have the protein’s primary sites of phosphorylation, and a C-terminal dimerization domain. Disruption of the communication between RNA and viral Nucleocapsid (N) protein can block the replication of the viral genome [7,8]. Therefore, Nucleocapsid (N) protein seems to be a good drug target. At present, several vaccines are at various stages of development to combat the spread of COVID-19 viz., NVXCoV2373, PittCoVacc,

Triple Antigen Vaccine, Ad5-nCoV, Coroflu, LV-SMENP-DC, ChAdOx1, mRNA-1273, BNT162b1 and INO-4800 [9-15]. In view of rapid mutations occurring in SARS-CoV-2 genome and development of several new potent variants (Alpha, Beta, Gamma, Delta, and Omicron) there is an urgent need to find effective drugs/ vaccines that can stop the SARS-CoV-2 viral multiplication. Using computational tools, several research groups have attempted to find potential drugs for the inhibition of COVID-19 virus. In the current study, computer-aided drug design (CADD) and pharmacophore-based virtual screening was used to screen novel inhibitors targeting Nucleocapsid(N)protein of SARS-CoV-2 from the Zinc database.

Materials & Methods

E-Pharmacophore Modeling and Molecular Docking

The 3D coordinates of Nucleocapsid (N) protein from SARSCoV- 2 (PDB ID: 6M3M) was retrieved [16] from PDB and preprocessed using Protein Preparation-wizard of Schrodinger version 5.5. During pre-processing, bond orders were assigned, unwanted water molecules were eliminated, formal charges and hydrogen atoms were added and all atom force field charges (OPLS3) were assigned to Nucleocapsid (N) protein. Receptor energy minimization was terminated when the energy converged or when the root mean square deviation reached a maximum cutoff of 0.30 Å [17,18]. In the structure based docking, crucial binding pocket residues of the Nucleocapsid (N) protein viz., THR 55, ALA 56, ARG 89, TYR 110, TYR 112 were utilized for grid box generation [16]. In the ligand preparation (i) OPLS-2005 force field was used, all conceivable ionization states at pH 7.0±2.0 were added, the desalt preference were created, tautomers were generated for all conformers. E-Pharmacophore mapping was performed using the scoring function against atom centers. The Glide XP conformers were specified as input to construct pharmacophore sites. The robust hypothesis remained subsequently utilized as a query for the database survey to identify potential inhibitors. A set of six innate chemical features, namely hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic group (H), negative ionizable region (N), positive ionizable region (P), and aromatic ring (R) were used for pharmacophore construction.

E-Pharmacophore Generation

For E-pharmacophore modelling, Nucleocapsid (N) protein inhibitors were selected based on the 3D structure of target protein (PDB ID: 6M3M) from protein databank. The prepared ligand and protein files were imported into Glide XP of Schrodinger software for docking analysis and the best conformer was subsequently used for E-pharmacophore construction. Schrödinger E-pharmacophore module was used to construct pharmacophore hypothesis. The constructed pharmacophore model comprises three-point pharmacophore hypotheses covering two hydrogen bond donors D3 & D4 and one hydrogen bond acceptor A1. The distances between pharmacophore sites A1 and D3, A1 and D4, and D3 and D4 were 2.452, 8.016, and 6.734, respectively. The pharmacophore site D3 was found in the cyclic amine NH group, acceptor A1 was found in the cyclic keto carbonyl CO group, and donor D4 was found in the side chain amine NH group of pj34. The overall screening flowchart (Figure 1), 2D structure of the reference ligand pj34 (Figure 2) and the distances and angles between the pharmacophore and their sites (Figures 3 & 4) were depicted below.

Chemical Warehouse Screening Based on Pharmacophore Features

Pharmacophore-based database screening is a rapid, accurate computational technique used in drug discovery to predict hit compounds with desirable pharmacokinetic properties [19]. Lipinski rules were applied to eliminate non-drug-like compounds; therefore, drugs with a molecular weight below 500 Da, a partition coefficient logP < 5, less than 5 hydrogen bond donors, less than 10 hydrogen bond acceptors were excluded. The ligands with suitable pharmacokinetic properties [20] were utilized to identify potential inhibitors of SARS-CoV-2 Nucleocapsid (N) protein. All the docking studies were carried out using Glide module. The Glide docking procedure involves three significant filtering phases: highthroughput virtual screening (HTVS), standard precision (SP), and extra precision (XP). Each docking mode, from HTVS to SP and SP to XP involves a Glide score, which represents the binding affinity used for ranking the ligands. A total of 4000 compounds screened from Zinc libraries based on the pharmacophore features were docked into the active site of SARS-CoV-2 Nucleocapsid (N) protein. Validated 3D pharmacophore models (ADD) of SARS-CoV-2 Nucleocapsid (N) protein inhibitors were used for the identification of competent molecules as well as coveted trait from large chemical warehouse i.e., Zinc database accompanied by fitness grade ranges >1 and 1000 hits were obtained based on the fitness score and pharmacophore matches.

Figure 1: Flowchart of methodology employed during insilico screening for identification of novel SARS CoVID-19 Nucleocapsid (N) protein inhibitors.

Figure 2: The 2D structures of the SARA-CoV-2 Nucleocapsid (N) protein inhibitor.

Figure 3: Three-point pharmacophore hypothesis of known Nucleocapsid (N) protein inhibitor with distance.

Figure 4: Three-point pharmacophore hypothesis of known Nucleocapsid (N) protein inhibitor with angles.

Prediction of ADME Properties

QikProp is a popular, fast, inexpensive, computational package that makes it easier to infer the pharmacokinetic properties of candidate drugs based on their molecular structures. The program predicts both physical significant descriptors and pharmaceutical pertinent traits. All the chemical entities were neutralized using QikProp application [21,22]. The default settings in the application were employed to determine the ADME properties of candidate molecules with desired pharmacokinetic properties.

Molecular Dynamics Simulation

Desmond simulation package was used to study the stability of docked SARS-CoV-2 Nucleocapsid (N) protein-ligand complex at a timescale of 100 ns [23]. Orthorhombic box was used to create a 10Å buffer region between the protein atoms and the box sides and the system was neutralized with Na and Cl ions. The OPLS3 force filed was used for energy minimization. The temperature was maintained constant at 300K, and a 2.0 fs values were obtained in the integration step [24-30]. Finally, molecular dynamics simulation was carried out at 100ns time scale for all four SARSCOV-2 Nucleocapsid (N) protein-hit compound complexes. The RMSD, RMSF and protein-ligand interactions were analyzed through simulation event analysis panel.

Results and Discussion

To evaluate the screening results, the reference ligand was downloaded from PubChem and docked it into the active site of Nucleocapsid (N) protein. During the docking interactions, it was observed that the amino acid residues GLN 189, THR 26 exhibited hydrogen bond interactions while LEU 27, CYS 145, MET 165 and MET 49 residues showed hydrophophic interaction with the reference compound PJ34. The Glide score and Glide energy of Nucleocapsid (N) protein-Reference ligand was -5.33 kcal/mol, -47.32kcal/mol respectively. The docking analysis initially resulted in screening of 500 compounds with HTVS. These compounds were further docked with the binding pocket of SARS-CoV-2 Nucleocapsid (N) protein in the SP mode. This led to identification of four potential ligands based on the Glide score. The four potential drug-like molecules had desirable pharmacokinetic properties and their structural representation was given below (Figures 5a-5d) (Table 1).

Figure 5a-d: 2D diagram of virtual screening hits a) ZINC55083699; b) ZINC12487932; c) ZINC81013764; d) ZINC68104820.

Table 1: Docking results of the four molecules.

Note: The IDs are of the ZINC database; Glide score (Kcal/mol); Glide energy (Kcal/mol); No of hydrogen bond interaction; Interacting residues; Distance between the protein and ligand (Å).

Docking Study

a. Binding Mode of ZINC55083699: The binding mode of ZINC55083699 indicates four hydrogen bond interactions within the active site of the SARS-CoV-2 Nucleocapsid (N) protein. The key amino acid residues of Nucleocapsid (N) protein involved in generating hydrogen bond network with ZINC55083699 compound includes PHE 54 (2.08Å), ARG 89 (2.34Å, 1.94Å), and SER 52 (2.20Å). The Glide score and Glide energy of the docked complex were -7.377kcal/mol and -51.264 kcal/mol respectively. Similarly, amino acids in the binding pocket such as ALA 157, VAL 159, ALA 91, TYR 112, PRO 169, and PRO 118 accounted for hydrophobic interactions. The crucial residues ARG 150, ARG 89 and LYS 66 were involved in the formation of pi-pi stacking and pi-cation interactions and all these interactions were responsible for increase in the binding stability of Nucleocapsid (N) protein- ZINC55083699 docked complex.

b. Binding Mode of ZINC12487932: The binding mode of ZINC12487932 in the active site of Nucleocapsid (N) protein indicates generation of hydrogen bonds with LYS 66 (2.25Å) and SER 52 (2.10Å, 2.13Å). The Glide score and Glide energy of the docked complex was found to be -6.239 kcal/mol and -45.803 kcal/ mol respectively. Similarly, PRO 68, ALA 51, VAL 159, TYR 110 and PRO 118 amino acids located in the binding pocket of Nucleocapsid (N) protein accounted for hydrophobic interactions.

c. Binding Mode of ZINC81013764: ZINC81013764 on binding in the active site of Nucleocapsid (N) protein exhibits three hydrogen bond interactions. The key amino acid residues involved in hydrogen bond includes LYS 66 (2.19Å), SER 52 (2.23Å), THR 50 (2.11Å) and TRP 133 (1.99Å) were involved in generating hydrogen bond network. The Glide scores and Glide energy of the docked complex were -6.153 kcal/mol and –53.469 kcal/mol respectively. The amino acids residues present at the binding pocket PRO 68, ALA 51, VAL 159, TYR 110, ILE 132 and TYR 112 were responsible for hydrophobic interactions in the docked Protein-ligand complex.

d. Binding Mode of ZINC68104820: The binding interaction of ZINC68104820 at the active site of Nucleocapsid (N) protein indicates generation of four hydrogen bonds due to the participation of Nucleocapsid (N) protein key amino acid residues ASN 154 (1.78Å), THR 50 (1.99Å, 2.03Å) and SER 52 (2.04Å). The Glide scores and Glide energy of the docked complex were -5.902 kcal/mol and -64.900 kcal/mol respectively. The hydrophobic interactions in the complex were due to ALA 51, PHE 54, ALA 56, TYR 112 and ALA 157 amino acid residues present at the binding pocket. Furthermore, interestingly only one pi-cation, pi-pi stacking interactions were observed with LYS 66 and ARG 150 amino acid residues (Figure 6).

Figure 6: 3-D interaction representation of the top four hit compounds a) ZINC55083699 b) ZINC12487932 c) ZINC81013764 d) ZINC68104820 in the active site of the Nucleocapsid (N) protein.

Prediction of ADME Properties

ADME describes the destiny of drug like compounds within living organisms especially in the human system. The pharmacokinetics traits of four ligands were tested using Qikprop module. All the four molecules satisfied drug-like traits based on Rule of Five. Their molecular mass was smaller than 500kDa, with H-bond donor below 5, H-bond acceptor below 10 and LogP value less than 5 and follows Lipinski rule for drug-like molecules. Concurrently, these four molecules were further tested for their drug-like behaviour through investigation of PK factors mandatory for absorption, dispersal, biotransformation, elimination and toxicity. The aqueous solubility (QPLogS) vital for appraisal of absorption and distribution of drug molecule inside the body ranged for the four molecules ranged between -6.258 to -2.823 respectively. The drug metabolism and penetration of biological membrane were assessed using cell permeability method (QPPcaco2) shows a range from~ 122 to ~652. The predicted value of QPPMDCK ranged from to 162 to 1126. The predicted values of binding to human serum albumin (QPlogksha) were within the acceptable ranges from -0.058 to 0.358. The predicted Brain/Blood barriers were under tolerable range from ~ -1.536 to -0.981, the predicted hydrophobic SASA were under acceptable range of 372.631 to 186.067. The predicted rotatable bonds were well with acceptable range 5.000 to 8.000. The % range of human oral absorption in GI was from 73 to 100. The predicted ADME properties results were displayed in the (Table 2).

Table 2: Drug-like properties of the new chemical entities determined by Qikprop.

Note: Molecular weight (under 500 Da); Hydrogen bond donors (lee then 5); Hydrogen bond acceptors (not more then 10); QP log P for octanol/water ( -2.0 / 6.5); Apparent Caco-2 Permeability (nm/sec) (<25 poor, >500 great); Apparent MDCK Permeability (nm/ sec) (<25 poor, >500 great); QP log S for aqueous solubility (-6.5 / 0.5); QP log K hsa Serum Protein Binding (acceptable range; -1.5 to 1.5); QP log BB for brain/blood ( -3.0 / 1.2); Solute Hydrophobic SASA (acceptable range; 0.0 to 750.0); Solute No. of Rotatable Bonds (acceptable range; 0.0 to 15.0); % Human Oral Absorption in GI (+-20%) (<25% is poor).

Molecular Dynamics (MD) Simulation

a) MD Analysis of Sars-Cov-2 Nucleocapsid (N) Protein - Zinc55083699 Complex: The binding stability of SARS-CoV-2 Nucleocapsid (N) protein–ZINC55083699 complex was examined using molecular dynamics simulations. The RMSD of protein-ligand complex was reported in figure 7a. At 15ns, a slight deviation was observed in c-alpha with an RMSD of 6.38Å and became stable till entire simulation with an observed RMSD value of 5.97Å. The root-mean-square fluctuations (RMSF) of the SARS-CoV-2 Nucleocapsid (N) protein were represented in figure 7b. The Cα, backbone and side chains displayed high RMSF values at GLY 98, ASP 99 with observed RMSF values as 10.86Å, 10.14Å and 10.50Å, respectively. The key residues THR 50, ASN 49 and ASP 99 in Cα backbone and side chains of SARS-CoV-2 Nucleocapsid (N) protein exhibited lower degree of fluctuations with RMSF values 2.54Å, 4.68Å and 9.98Å respectively. The bar diagram of 7c represents SARS-CoV-2 Nucleocapsid (N) protein-ZINC55083699 interactions. Hydrogen bond interactions were observed with ASN 70, TYR 124 amino acid residues and the occupancy was noted 11.8%, and 33.7% respectively. The overall Nucleocapsid (N) protein-ligand interactions observed during the simulation presented in (Figure 7d). This indicates that key amino acid residues were involved in the formation of hydrogen bond (ASN 78, ALA 156 and ASN 76 for 13%, 13% and 20%,) and water bridge contact (TYR 124 for 15%) with Nucleocapsid (N) protein leading to increased binding stability of the Nucleocapsid (N) protein-ligand complex during the simulation time (Figure 7).

Figure 7: a). The RMSD plot of SARSCOV-2 Nucleocapsid (N) protein-ZINC55083699 complex during 100-ns simulations. b). The RMSF plot of SARSCOV-2 Nucleocapsid (N) protein during the 100ns simulations. c) The RMSF map of SARSCOV-2 Nucleocapsid (N) protein - ZINC55083699 complex over the 100ns simulations. d). The bar diagrams of SARSCOV-2 Nucleocapsid (N) protein - ZINC55083699 contacts during 100ns simulations.

b) MD analysis of SARS-CoV- Nucleocapsid (N) protein – ZINC12487932 complex: The RMSD of protein-ligand complex was reported in Figure 8a. There was no deviation observed in the protein RMSD but, a slight deviation was observed in ligand RMSD at 61.40 ns (5.04Å), 75ns (6.86Å) and 99.90ns (6.59Å). The rootmean- square fluctuations (RMSF) of the SARSCOV-2 Nucleocapsid (N) protein were represented in figure 8b. The Cα, backbone and side chain displayed high RMSF values in the position of GLY 98 (10.49Å), GLY 98 (10.18Å) and ASP 99 (10.17Å) respectively. The bar diagram of 8c represents SARSCOV-2 Nucleocapsid (N) protein- ZINC12487932 interactions. The hydrogen bond interactions were observed with SER 52, TYR 112 and the occupancy was noted as 56% and 61.4% respectively. The key amino acid residues SER 52, PHE 54 and TYR 112 were involved in the formation of hydrogen bond to the extent of 46%, 37% and 31% respectively. A water bridge contact with LYS 66 of target protein was observed as 42% and also ligand formed only one pi-pi stacking interaction with TYR 110. These molecular interactions at the binding site region of the SARSCOV-2 Nucleocapsid (N) protein lead to the improved binding stability of the Nucleocapsid (N) protein-ZINC12487932 complex during simulation time. (Figure 8).

Figure 8: a). The RMSD plot of SARSCOV-2 Nucleocapsid (N) protein-ZINC12487932 complex during 100-26 ns simulations.

b). The RMSF plot of SARSCOV-2 Nucleocapsid (N) protein during the 100ns simulations. c) The RMSF map of SARSCOV-2 Nucleocapsid (N) protein - ZINC12487932 complex over the 100ns simulations. d). The bar diagrams of SARSCOV-2 Nucleocapsid (N) protein - ZINC12487932 contacts during 100ns simulations.

c) MD Analysis of Sars-Cov-2 Nucleocapsid (N) Protein – Zinc81013764 Complex: The RMSD of Nucleocapsid (N) protein -ligand complex was reported in figure 9a. A slight RMSD deviation was observed in c-alpha at 37.20ns followed by a more stable RMSD value of 5.79Å till the end of entire simulation. The root-meansquare fluctuations (RMSF) of the SARSCOV-2 Nucleocapsid (N) protein was represented in (Figure 9b). The Cα, backbone and side chain displayed high RMSF values in the amino acid residues of GLY 100, ASP 99, GLY 98 with RMSF values 10.54Å, 10.20Å and 11.54Å, respectively. The key residues GLY 98, THR 50 and MET 102 in the Cα atoms, backbone and side chain SARSCOV-2 Nucleocapsid (N) protein exhibited lower degree of fluctuations with RMSF values 8.58Å, 2.27Å and 6.20Å respectively. The bar diagram of 9c were represent the SARSCOV-2 Nucleocapsid (N) protein-ZINC81013764 interactions. A hydrogen bond interaction was observed with SER 32, TYR 110 and the occupancy was noted 75.9% and 76.6% respectively. In addition, figure 9d indicates key amino acid residues SER 52 and ASN 154 involved in the formation of hydrogen bond (52% and 54%) and water bridge contact ARG 89(82%), TYR 112 (80%) with Nucleocapsid (N) protein and one pi-cation interaction with LYS (66 44%). The molecular interactions contributed to the enhanced binding stability of the protein-ligand complex during the simulation time (Figure 9).

Figure 9: a). The RMSD plot of SARSCOV-2 Nucleocapsid (N) protein - ZINC81013764 complex during 100-ns simulations.

b). The RMSF plot of SARSCOV-2 Nucleocapsid (N) protein during the 100ns simulations. c) The RMSF map of SARSCOV-2 Nucleocapsid (N) protein-ZINC81013764 complex over the 100ns simulations. d). The bar diagrams of SARSCOV-2 Nucleocapsid (N) protein - ZINC81013764 contacts during 100ns simulations.

d) MD Analysis of Sars-Cov-2 Nucleocapsid (N) Protein- Zinc68104820 Complex: The RMSD of protein-ligand complex was given in Figure 10a. The RMSD of the c-alpha was more stable during the entire simulation period and the RMSD observed was 7.73Å. Ligand RMSD exhibited slight deviation at 25ns- 31.20ns and the RMSD values observed were 8.14 to 8.28Å respectively and was stable during the remaining simulation period was with RMSD reaching 4.08Å. The root-mean-square fluctuations (RMSF) of the SARSCOV-2 Nucleocapsid (N) protein–ZINC68104820 were presented in figure 10b. The Cα, backbone and side chain displayed high RMSF values in the position of GLY 98 (8.65Å). Hydrogen bond interactions were observed with ARG 150 and the occupancy 74.9% occupancy value. In addition, the key amino acid residue ARG 150 was involved in the formation of pi-pi stacking (38% and 42%) and water bridge contact (43%) with target protein. These molecular interactions contributed to the stability of the Nucleocapsid (N) protein-Ligand complex during the simulation period (Figure 10).

Figure 10:

a. The RMSD plot of SARSCOV-2 Nucleocapsid (N) protein - ZINC68104820 complex during 100-ns simulations.

b. The RMSF plot of SARSCOV-2 Nucleocapsid (N) protein during the 100ns simulations.

c. The RMSF map of SARSCOV-2 Nucleocapsid (N) protein - ZINC68104820 complex over the 100ns simulations.

d. The bar diagrams of SARSCOV-2 Nucleocapsid (N) protein - ZINC68104820 contacts during 100ns simulations.

Conclusion

Nucleocapsid (N) protein is an essential structural protein mainly responsible for the interaction, transcription, and invasion of the SARS-CoV-2 viral genome and is considered as an attractive drug target for the treatment of COVID-19. In the current study, four potential Nucleocapsid (N) protein inhibitors viz., ZINC55083699, ZINC12487932, ZINC81013764 and ZINC68104820 were identified through E-pharmacophore from the zinc database. All the Pharmacokinetic properties of these 4 compounds met the required criteria. Molecular Dynamics simulation for 100 ns revealed all the 4 compounds to be stable in the solvent environment with respect to distance and fluctuation dynamics. These 4 compounds on further studies could be used as novel leads for the inhibition of Nucleocapsid (N) protein.

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