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Pembrolizumab

Most cited protocols related to «Pembrolizumab»

Cited 124 times

After receiving institutional review board approval from the Memorial Sloan Kettering Cancer Center, institutional pharmacy records were used to identify patients who received at least one dose of immunotherapy (atezolizumab, avelumab, durvalumab, ipilimumab, nivolumab, pembrolizumab, or tremelimumab) and then cross-referenced with patients who had MSK-IMPACT testing done in the context of routine clinical care. Cancer types with greater than 35 patients on initial collection were selected for further analysis in the cohort. The majority of patients who received MSK-IMPACT testing on tumor tissue are enrolled on an institutional IRB-approved research protocol (NCT01775072) with the remaining patients receiving testing as part of routine clinical care; all patients provided informed consent permitting return of results from sequencing analyses and broader characterization of banked specimens for research.Details of tissue processing and next generation sequencing and analysis have been previously described. ^{11 (link)} Importantly, concurrent sequencing of germline DNA from peripheral blood is performed for all samples to identify somatic tumor mutations. Patients enrolled on ongoing clinical trials for which publication of outcomes data was prohibited were removed as well as a small proportion of patients with localized disease treated in the neoadjuvant setting(n=9) or who had localized disease. Other preceding or concurrent non-ICI treatments were not recorded or accounted for in the analysis. The timing of tissue pathology on which MSK-IMPACT was performed relative to ICI administration is also heterogenous with a small portion of patients with testing after ICI administration.

Samstein R.M., Lee C.H., Shoushtari A.N., Hellmann M.D., Shen R., Janjigian Y.Y., Barron D.A., Zehir A., Jordan E.J., Omuro A., Kaley T.J., Kendall S.M., Motzer R.J., Hakimi A.A., Voss M.H., Russo P., Rosenberg J., Iyer G., Bochner B.H., Bajorin D.F., Al-Ahmadie H.A., Chaft J.E., Rudin C.M., Riely G.J., Baxi S., Ho A.L., Wong R.J., Pfister D.G., Wolchok J.D., Barker C.A., Gutin P.H., Brennan C., Tabar V., Mellinghoff I.K., DeAngelis L.M., Ariyan C.E., Lee N., Tap W.D., Gounder M.M., D’Angelo S.P., Saltz L., Stadler Z.K., Scher H.I., Baselga J., Razavi P., Klebanoff C.A., Yaeger R., Segal N.H., Ku G.Y., DeMatteo R.P., Ladanyi M., Rizvi N.A., Berger M.F., Riaz N., Solit D.B., Chan T.A, & Morris L.G. (2019). Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nature genetics, 51(2), 202-206.

Publication 2019

atezolizumab avelumab BLOOD Diploid Cell durvalumab Genetic Heterogeneity Germ Line Immunotherapy Ipilimumab Malignant Neoplasms Mutation Neoadjuvant Therapy Neoplasms Nivolumab Patients pembrolizumab Sequence Analysis Tissues tremelimumab

Comprehensive Genomic Profiling of Anti-PD-1 Treated Melanoma

Cited 86 times

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Publication 2016

Biopsy Diploid Cell Ethics Committees, Research Exome Gene Expression Profiling Melanoma Missense Mutation Mutation Neoplasms Neoplasms, Second Primary Nivolumab Patients pembrolizumab RNA, Neoplasm Tissues

Immune and Tumor Profiling in NSCLC and Follicular Lymphoma

Cited 66 times

All patient samples in this study were collected with informed consent for research use and were approved by the Stanford Institutional Review Board in accordance with the Declaration of Helsinki. For a patient with metastatic NSCLC treated with an immune checkpoint inhibitor (Pembrolizumab, Merck), peripheral blood was obtained on the first day of treatment prior to infusion (Fig. 2a; NCT00349830 and NCT02955758). Fresh tumor biopsies from patients with early stage NSCLC were obtained during routine primary surgical resection (Figs. 3g and 5, Supplementary Fig. 13c-f). Fresh or frozen surgical biopsies of follicular lymphoma tumors were obtained from previously untreated FL patients enrolled in a phase III clinical trial (NCT00017290^{63 (link)}), as well as from patients seen as part of the Stanford University Lymphoma Program Project (NCT00398177; Figs. 3b-f, 5a-c, Supplementary Figs. 6a-c, 14). Whole blood samples from 12 healthy adult donors were obtained from the Stanford Blood Center (Fig. 2b,e and Supplementary Figs. 1d,k,l, 2a,d).

Newman A.M., Steen C.B., Liu C.L., Gentles A.J., Chaudhuri A.A., Scherer F., Khodadoust M.S., Esfahani M.S., Luca B.A., Steiner D., Diehn M, & Alizadeh A.A. (2019). Determining cell-type abundance and expression from bulk tissues with digital cytometry. Nature biotechnology, 37(7), 773-782.

Publication 2019

Adult Biopsy BLOOD Donor, Blood Ethics Committees, Research Figs Freezing Immune Checkpoint Inhibitors Lymphoma Lymphoma, Follicular Neoplasms Non-Small Cell Lung Carcinoma Operative Surgical Procedures Patients pembrolizumab Vision

Immune and Tumor Profiling in NSCLC and Follicular Lymphoma

Cited 36 times

Publication 2019

PD-L1 Protein Expression Assessment

Cited 32 times

For determination of PD-L1 protein expression, positivity was defined as complete circumferential or partial cell membrane staining of viable tumor cells with 1+ to 3+ intensity. Nonspecific staining was recorded on a 0 to 3 intensity scale, in 0.25 grade increments. Tumor-associated immune cells were excluded from PD-L1 scoring.16 Cytoplasmic staining, if present, was excluded from the scoring. Scoring was recorded as percentage of PD-L1-positive tumor cells over total tumor cells in the denominator (TPS). NSCLC specimens stained with the negative control reagent must have 0 specific membrane staining and ≤1+ intensity nonspecific (nonmembrane) staining.

Roach C., Zhang N., Corigliano E., Jansson M., Toland G., Ponto G., Dolled-Filhart M., Emancipator K., Stanforth D, & Kulangara K. (2016). Development of a Companion Diagnostic PD-L1 Immunohistochemistry Assay for Pembrolizumab Therapy in Non–Small-cell Lung Cancer. Applied Immunohistochemistry & Molecular Morphology, 24(6), 392-397.

Publication 2016

Antigens, CD274 CD274 protein, human Cells Cytoplasm Neoplasms Non-Small Cell Lung Carcinoma Plasma Membrane Tissue, Membrane

Most recents protocols related to «Pembrolizumab»

Pembrolizumab and Bevacizumab in Radiotherapy

Pembrolizumab was infused intravenously at a dose of 200 mg, every 3 weeks, according to KEYNOTE-240 trial (17 (link)). Bevacizumab was administered intravenously over 90 minutes by 15 mg/kg bodyweight every 3 weeks (5 (link)). Pembrolizumab and bevacizumab were infused within 2 weeks before or after the start of radiotherapy.

Liang X., Jiang Y., Yao W., Deng Y., Yang S, & Liu Q. (2024). Liver-directed moderately hypo-fractionated radiotherapy combined with pembrolizumab and bevacizumab for advanced hepatocellular carcinoma: a retrospective observational study of 23 cases. Translational Cancer Research, 13(3), 1508-1518.

Publication 2024

Balancing Covariates in Clinical Trials

Not available on PMC !

Sundahl et al. ( 2019) studied the safety of pembrolizumab when combined with sequential or concomitant body radiotherapy in metastatic bladder cancer. The response under study was the dose-limiting toxicity measured on a specific scale. The clinical trial consisted of 18 subjects whose prognostic factors included age, sex, hemoglobin concentration, a modified proportion score of PD-L1, smoking status, among others. The subjects were enrolled into two equally sized groups. Groups one and two were administered pembrolizumab using sequential and concomitant body ratiotherapy, respectively.
We compare the actual assignment of the subjects to the two groups of Sundahl et al.
(2019) with the minimization methods to balance the continuous covariates: age, the modified proportion score of PD-L1, and hemoglobin concentration. To evaluate the methods, we standardized the covariates to have a mean of zero and a standard deviation of one. We then applied each method 1,000 times to balance the covariates in the standardized set. Regarding the absolute difference between the group sizes, Figure 1 shows that the median absolute difference is two for the PS, NT, and MH methods. In contrast, the group absolute differences obtained by the BMW method are all equal to zero. This is because, in contrast to the other methods, the BMW method enforces group size balance.
Figure 2a shows that all minimization methods improve the actual group assignment for the absolute difference between group means of age. This is because at least 75% of the group mean differences for all methods are below the dashed line. The BKW method has a lower median and third quartile of group mean differences than the rest.
Figure 2b shows that all methods are better than the actual group assignment to balance the standard deviations of age, because at least 75% of their absolute differences in the group standard deviations are below that obtained from the actual assignment. The NT method is better than the others because both its median and third quartile of group mean differences are smaller than those of the other methods.
Regarding the group means for PD-L1, Figure 3a shows that all minimization methods improve the actual assignment of groups, since 75% of their absolute group mean differences are below the observed in the clinical trial. In this case, the NT method is the most successful because it has a lower median absolute group difference than the others. Regarding the group standard deviations for PD-L1, Figure 3b shows that at least 50% of the group absolute differences obtained from the methods are smaller than the actual group difference in the clinical trial. The NT method is again better than the rest because its median absolute difference in group standard deviation is smaller than the others.
Regarding hemoglobin concentration, Figure 4a shows that, to a large extent, all methods have greater absolute group mean differences than the one observed in the clinical trial. This is because either the first or second quartile of each method is above the dashed line in the figure. For the absolute group difference of the standard deviations, Figure 4b shows that about 50% of the group differences for all methods are below the actual group difference observed. In any case, the BKW method performs slightly better than the other methods for this covariate, as its median group differences are smaller than the others.
Figure 5 shows that all methods are better at balancing the joint distribution of the covariates between the two groups. This is because at least 75% of the energy distance values for all methods are smaller than 0.671, which was observed from the actual group assignment. The BKW and NT methods are the best in terms of the energy distance, since they have a lower median energy distance value than the rest. However, the BKW method has slightly lower median and third quartile values than the NT method.
Regarding randomness in assigning subjects to groups, Figure 6 shows that the median values of the mean CG probability are the same for all methods. However, the NT method tends to have a smaller mean CG probability than the rest because its first and third quartiles are smaller than those of the others. In any case, the first quartile of the mean CG probability is at least 1/2 for all methods.
4.3 The infant spasms clinical trial Chiron et al. (1997) studied the effect of vigabatrin on spasms due to tuberous sclerosis in infants. To this end, they conducted a clinical trial with 22 infants whose prognostic factors included age, sex, and duration and frequency of spasms. As an alternative treatment, Chiron et al. (1997) used hydrocortisone which is a standard steroid. In the trial, the infants were enrolled into two equally sized groups. One group was treated with vigabatrin while the other was treated with hydrocortisone. To assess the efficacy of the treatments, the authors used the time to disappearance of the spasms, the tolerability to the treatment, the evolution of the development quotient, among other responses.
We compare the actual group assignment of Chiron et al. (1997) with minimization methods to balance the continuous covariates: frequency of infantile spasms (FIS) and age.
Similarly to the pembrolizumab trial, we standardized these covariates to have a mean of zero and a standard deviation of one, and executed each method 1,000 times. Regarding the balance in the group sizes, Figure 7 shows that the BKW method is better than the others because all its group size differences are zero. In contrast, the other methods have a median absolute difference between group sizes equal to two.
Figure 8a shows that all minimization methods improve the actual group assignment in terms of the mean of the FIS. This is because virtually all their absolute differences are smaller than the actual one indicated by the dashed line in the figure. The NT method has the smallest median absolute group difference. Regarding the absolute difference in the standard deviations for the FIS, Figure 8b shows that the BKW method has smaller first and second quartiles than those of the other methods. However, none of the methods outperforms the actual group assignment in this case. This is because the first quartiles of the absolute group differences of all methods are greater than the difference observed in the clinical trial.
Figure 9 shows that the BWK and NT methods are generally better than the others in terms of balancing age, since at least 75% of their group differences are below the differences observed in the trial. For the absolute difference in group means, the NT method has the lowest median, but the BWK has the smallest third quartile and dispersion; see Figure 9a.
For the differences between group standard deviations, the BKW method has a median and third quartile that are smaller than those of the other methods; see Figure 9b.
Figure 10 shows that all methods are better than the actual clinical trial in terms of the energy distance. This is because the energy distance values of all the methods are smaller than the observed energy distance value, which was 0.79. The BKW method is the best in this case, as its first and third quartiles are smaller than those of the other methods.
Figure 11 shows that the MH method has smaller mean CG probability values than the other methods. This is because all its quartiles are smaller than those of the others.
However, none of the methods succeed in minimizing the mean CG probability, since more than 75% of their values are higher than or equal to 1/2.

Vazquez A.R, & Wong W.K. (2024). Mathematical programming tools for randomization purposes in small two-arm clinical trials: A case study with real data. Pharmaceutical statistics.

Publication 2024

Site-of-Action Pharmacokinetic-Pharmacodynamic Modeling

Experiments aimed at characterizing the dose-dependent relationship between drug concentration and tumor size form the backbone of pre-clinical studies in oncology. Typically, the collected time-course measurements are tumor volume and drug concentration in the plasma, which are phenomenologically captured by a simple indirect response model, such as ref. ^{30 (link)}. This correlation might be sufficient for assessing the general dose-response relationship but cannot answer the question of whether the underlying mechanism of action of the drug has been fully engaged.
For this question, we often use pharmacobinding (PB) models^{31 (link)}, which describe the dynamics of the target (such as PD-1 for pembrolizumab), and the reversible binding kinetics between the drug and its target. This allows calculating levels of projected target occupancy, and it is typically expected that if over 90% of the target has been engaged without an effect, then the target may not be the correct one for the selected indication^{31 (link)}. Such PK-PB models can facilitate the development of a mechanistic understanding of the dose-response relationship between the drug and the tumor size.
A step further can be taken with site-of-action models^{32 (link)–34 (link)} that take into account the drug-target dynamics not only in the plasma but also, as the name suggests, at the site of action, such as the TME. These models can vary in degrees of complexity from more mechanistic³⁵ to more detailed physiologically based pharmacokinetic models^{36 (link),37 (link)}. While such models can be used to calculate projected levels of target occupancy in the TME, it is unclear whether these estimates are truly reliable without actually sampling the TME, the question we will be addressing here.
For that, we developed a modified version of a two-compartment site-of-action model which describes drug concentration over time in the central (plasma), peripheral (tissue), and TME compartments. We assume that pharmacobinding occurs in the plasma and TME compartments; while it is possible that some drug-target dynamics occur in the peripheral compartment as well, we assume that it is either negligible with regards to overall dose-response dynamics or cannot be measured; these assumptions can be relaxed if needed.
The model has a standard structure in the plasma compartment, with an assumption of intravenous drug administration that is cleared at a rate

k_{10}

; the drug distributes to the peripheral compartment at a rate

(V_{1} / V_{2}) k_{12}

and back at a rate

(V_{2} / V_{1}) k_{21}

, where

V_{1}

is the volume of distribution in the central compartment, and

V_{2}

is the volume of distribution in the peripheral compartment. We assume that the free target

T_{p}

is synthesized in the plasma at a rate

k_{syn}

and, since the model is calibrated to pembrolizumab data whose target PD-1 is membrane-bound, we assume that it is cleared primarily through internalization at a rate

k_{int P}

. We also assume reversible binding kinetics between the drug and its target, with the drug-target complex in the plasma forming at a rate

k_{on}

, dissociating at a rate

k_{off},

and clearing at the rate

k_{int P}

.
The PK-PB dynamics in the TME compartment are largely similar, with several proposed modifications. Firstly, we assume that the rate of drug distribution into the TME is not constant but is a function of the tumor volume, namely,

(V_{1} / (x + δ)) k_{1 T}

, where

x

is tumor volume and

δ

is introduced to prevent division by zero in the limiting case, where the tumor volume tends to zero. We propose that while

V_{1}

and

V_{2}

are treated as constant volumes of distribution (as is standard), the volume of distribution into the tumor be treated as variable, thereby capturing the higher or lower distribution of the drug into the TME depending on tumor size. As a consequence of this assumption, we further propose that the rate of target synthesis in the tumor is not constant or at equilibrium as would likely be in the plasma or non-disease compartment, but instead is treated as a function of tumor size. In particular, we assume this rate increases according to a saturating function

\frac{k_{synt} x}{x + K_{x}}

, where

k_{synt}

is the rate of target synthesis in the tumor (which is likely higher than in the plasma), and

K_{x}

is the half-maximal concentration of free target

T_{TME}

in the TME.
Additionally, we hypothesize that the apparent rate of drug-target binding in the TME is not necessarily the same as in the plasma, i.e., that

k_{onT}

may be different from

k_{on}

. That said, we expect that once the drug-target complex has been formed, the dissociation rate

k_{off}

will remain the same, as that is more likely to be an intrinsic property^{38 (link)}. Finally, we assume that the tumor grows logistically and is killed as a function of the percent target occupancy in the tumor, which is calculated as

\frac{D R_{TME}}{D R_{TME} + T_{TME}}

, where

D R_{TME}

is the concentration of the drug-target complex in the tumor and

T_{TME}

is the free target in the TME.
The resulting system of equations is as follows:

(\begin{matrix} \frac{d D_{p}}{d t} = u (t) - k_{10} D_{p} - k_{o n} D_{P} T_{p} + k_{o f f} D_{R} - k_{12} D_{p} + k_{21} \frac{V_{2}}{V_{1}} D_{t} \\ - k_{1 T} D_{p} + k_{T 1} {\frac{x + δ}{V_{1}} D}_{T M E} \\ \frac{d T_{p}}{d t} = k_{s y n} - k_{i n t P} T_{p} - k_{o n} D_{p} T_{P} + k_{o f f} D_{R} \\ \frac{d D_{R}}{d t} = k_{o n} D_{p} T_{P} - k_{o f f} D_{R} - k_{i n t P} D_{R} \\ \frac{d D_{t}}{d t} = k_{12} \frac{V_{1}}{V_{2}} D_{p} - k_{21} D_{t} \end{matrix}\} Non - tumor compartments

(\begin{matrix} \frac{d D_{T M E}}{d t} = k_{1 T} {\frac{V_{1}}{x + δ} D}_{P} - k_{T 1} D_{T M E} - k_{o n T} D_{T M E} T_{T M E} + k_{o f f} D R_{T M E} \\ \frac{d T_{T M E}}{d t} = \frac{k_{s y n t} x}{x + K_{x}} - k_{i n t} T_{T M E} - k_{o n T} D_{T M E} T_{T M E} + k_{o f f} D R_{T M E} \\ \frac{d D R_{T M E}}{d t} = k_{o n T} D_{T M E} T_{T M E} - k_{o f f} D R_{T M E} - k_{i n t} D R_{T M E} \\ T O_{T M E} = 100 \times \frac{D R_{T M E}}{D R_{T M E} + T_{T M E}} \\ \frac{d x}{d t} = r x (1 - \frac{x}{K}) - d \frac{T O_{T M E}}{T O_{T M E} + T O_{50}} x \end{matrix}\} TME compartment

The structure of the model is summarized in Fig. 1. Variable definitions, initial conditions, and calibrated parameter values are summarized in Table 1.
The model was calibrated to digitized PK data (Fig. 2A) for pembrolizumab reported in ref. ^{13 (link)} and TGI data reported in ref. ²⁰. The reason this particular PK dataset was chosen is that it includes measurements of percent TO in the TME, which is typically not available. TGI curves in²⁰ are measured for 2 mg/kg and 10 mg/kg of pembrolizumab, administered on average 3.5 days apart, for C57BL/6 mice implanted with MC38 syngeneic colon adenocarcinoma cells. We further calibrated model parameters to fit the PK-TGI relationship for the dose of 10 mg/kg (Fig. 2B). We also report the projected levels of percent TO in plasma as compared to the TME (Fig. 2C) to emphasize the importance of capturing drug-target dynamics in the TME, as this is where it is expected to drive efficacy.
Model parameterization was validated using untrained data. Figure 2D demonstrates that we were able to successfully recapitulate the PK curves for three doses of 1 mg/kg of pembrolizumab given weekly^{13 (link)}, and Fig. 2E shows that we were able to describe the TGI data for five doses of 2 mg/kg of pembrolizumab given on average every 3.5 days²⁰. We note that, without the %TO in TME data (Fig. 2F), there was a large number of parameter sets that could recapitulate the PK and TGI equally well, further emphasizing that this piece of data was critical to model parameterization.

Gevertz J.L, & Kareva I. (2024). Minimally sufficient experimental design using identifiability analysis. NPJ Systems Biology and Applications, 10, 2.

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Publication 2024

Comparative Effectiveness Analysis of Toripalimab and Pembrolizumab

Not available on PMC !

Given the assumption of equivalence in efficacy and safety between the toripalimab and pembrolizumab regimens (necessitated by the lack of pembrolizumab data in combination with gemcitabine and cisplatin), two scenario analyses were conducted on the ASP model in which the efficacy and safety data were varied: 1) to favor pembrolizumab in terms of efficacy and safety, and 2) to favor toripalimab in terms of efficacy and safety. For the first scenario analysis favoring pembrolizumab, the pembrolizumab inputs for progression rate, discontinuation rate, and AE rates were set to -20% of the base case and the median PFS was set to þ20%. In the second scenario analysis favoring toripalimab, the pembrolizumab inputs for progression rate, discontinuation rate, and AE rates were set to þ20% of the base case and the median PFS was set to -20%.

Abraham I., Calamia M., Alkhatib N., Pondel M, & MacDonald K. (2024). Budget impact analysis of the novel PD-1 inhibitor toripalimab versus pembrolizumab in recurrent or metastatic nasopharyngeal carcinoma. Journal of medical economics, 27(sup3).

Publication 2024

Improving Pembrolizumab Predictive Value

We explore the consequence of improving the predictive value of TMB for treatment benefit of pembrolizumab (with or without ChT). The predictive value of TMB was varied by increasing the treatment benefit in the responders’ group (i.e., TMB high) and decreasing the benefit in the non-responders’ group (i.e., TMB low). The magnitude of increase was 0.25 years to 1.75 years, in steps of 0.25 years. Further details are provided in Sect. 1.6 of the ESM.

Mfumbilwa Z.A., Simons M.J., Ramaekers B., Retèl V.P., Mankor J.M., Groen H.J., Aerts J.G., Joore M., Wilschut J.A, & Coupé V.M. (2024). Exploring the Cost Effectiveness of a Whole-Genome Sequencing-Based Biomarker for Treatment Selection in Patients with Advanced Lung Cancer Ineligible for Targeted Therapy. Pharmacoeconomics, 42(4), 419-434.

Publication 2024

Top products related to «Pembrolizumab»

Pembrolizumab by Merck Group

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Pembrolizumab is a monoclonal antibody used in laboratory research. It targets the PD-1 receptor, a protein that regulates the immune system's response to cancer cells. Pembrolizumab is used to study the role of the PD-1 pathway in various biological processes.

Pd l1 ihc 22c3 pharmdx assay by Agilent Technologies

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The PD-L1 IHC 22C3 pharmDx assay is a semi-quantitative immunohistochemical assay used to detect the programmed death-ligand 1 (PD-L1) protein in formalin-fixed, paraffin-embedded (FFPE) tissue specimens. The assay utilizes the 22C3 anti-PD-L1 antibody and is intended for in vitro diagnostic use.

Nivolumab by Bristol-Myers Squibb

Sourced in United States, China

Nivolumab is a monoclonal antibody that targets the programmed cell death-1 (PD-1) receptor. It is designed to block the interaction between PD-1 and its ligands, thereby enhancing the immune system's ability to detect and respond to cancer cells.

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Sourced in United States, Denmark

The PD-L1 IHC 22C3 pharmDx is a qualitative immunohistochemical assay used to detect the programmed death-ligand 1 (PD-L1) protein in formalin-fixed, paraffin-embedded (FFPE) tumor tissue. The assay utilizes the 22C3 anti-PD-L1 primary antibody and a chromogenic detection system.

Pembrolizumab by Selleck Chemicals

Sourced in United States

Pembrolizumab is a monoclonal antibody used as a laboratory reagent. It is designed to bind to the PD-1 receptor on T cells, which can help to enhance the immune response.

Sas version 9 by SAS Institute

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Keytruda by Merck Group

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Keytruda is a lab equipment product manufactured by the Merck Group. It is designed to facilitate specific laboratory procedures and analyses. The core function of Keytruda is to assist in the research and development of pharmaceutical and medical products, without further interpretation of its intended use.

Prism 8 by GraphPad

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Prism 8 is a data analysis and graphing software developed by GraphPad. It is designed for researchers to visualize, analyze, and present scientific data.

Fetal bovine serum (fbs) by Thermo Fisher Scientific

Sourced in United States, China, United Kingdom, Germany, Australia, Japan, Canada, Italy, France, Switzerland, New Zealand, Brazil, Belgium, India, Spain, Israel, Austria, Poland, Ireland, Sweden, Macao, Netherlands, Denmark, Cameroon, Singapore, Portugal, Argentina, Holy See (Vatican City State), Morocco, Uruguay, Mexico, Thailand, Sao Tome and Principe, Hungary, Panama, Hong Kong, Norway, United Arab Emirates, Czechia, Russian Federation, Chile, Moldova, Republic of, Gabon, Palestine, State of, Saudi Arabia, Senegal

Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.

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Opdivo is a prescription laboratory equipment product. It is an immunotherapy agent that acts as a PD-1 inhibitor.

What is Pembrolizumab?

How can PubCompare.ai help with Pembrolizumab research?

PubCompare.ai allows you to screen protocol literature more efficiently and leverage AI to pinpiont critical insights. The platform's AI-driven analysis can help researchers identify the most effective protocols related to Pembrolizumab for their specific research goals, highlighting key differences in protocol effectiveness to enable them to choose the best option for reproducibility and accuracy. This can streamline the Pembrolizumab research process and help achieve more reliable results.

What are the different variations or types of Pembrolizumab?

Pembrolizumab is a specific monoclonal antibody that targets the PD-1 receptor. There are no major vatirations or subtypes of Pembrolizumab itself. However, Pembrolizumab may be used in combination with other therapies, such as chemotherapy or other immunotherapies, depending on the cancer type and stage of disease being treated. The specific treatment regimen and combination of medications can vary based on the patient's individual needs and the recomendations of their healthcare team.

What are the common applications of Pembrolizumab?

Pembrolizumab is primarily used to treat various types of cancer, including melanoma, non-small cell lung cancer, head and neck squamous cell carcinoma, classical Hodgkin lymphoma, primary mediastinal large B-cell lymphoma, urothelial carcinoma, microsatellite instability-high (MSI-H) or mismatch repair deficient (dMMR) solid tumors, esophageal cancer, cervical cancer, and hepatocellular carcinoma. It may be used as a single agent or in combination with other therapies, depending on the specific cancer type and stage of disease.

What are some common challenges in using Pembrolizumab?

Some common challenges in using Pembrolizumab include managing immune-related adverse events, such as pneumonitis, colitis, hepatitis, and endocrinopathies. Patients receiving Pembrolizumab require close monitoring and prompt management of any side effects. Additionally, not all patients respond equally well to Pembrolizumab, and identifying predictive biomarkers to select the right patients for this therapy remains an area of active research. Expereince the future of research optimization with PubCompare.ai to help address these challenges and improve the use of Pembrolizumab.

More about "Pembrolizumab"

Pembrolizumab is a monoclonal antibody that targets the programmed cell death 1 (PD-1) receptor, a key regulator of the immune system.
It is used to treat various types of cancer, including melanoma, non-small cell lung cancer, head and neck squamous cell carcinoma, and others.
Pembrolizumab, also known as Keytruda, works by blocking the interaction between PD-1 and its ligands, PD-L1 and PD-L2, thereby restoring the immune system's ability to recognize and destroy cancer cells.
This immune checkpoint inhibitor has been shown to improve overall survival and progression-free survival in clinical trials.
Pembrolizumab is often used in combination with other therapies, such as the PD-L1 IHC 22C3 pharmDx assay, which is used to detect PD-L1 expression and guide treatment decisions.
In addition to Pembrolizumab, other PD-1/PD-L1 inhibitors like Nivolumab (Opdivo) are also used in cancer treatment.
These immunotherapies work by targeting the PD-1/PD-L1 pathway, which helps the immune system recognize and attack cancer cells more effectively.
Researchers can use advanced AI-driven platforms like PubCompare.ai to optimize their Pembrolizumab studies and achieve more reliable results.
PubCompare.ai can help locate the best protocols from literature, pre-prints, and patents using advanced AI comparisons, enhancing reproducibility and accuracy.
By leveraging the power of AI, researchers can streamline their Pembrolizumab research process and achieve more reliable outcomes, all while using tools like SAS version 9.4 and Prism 8 for data analysis and visualization.