Journal of Bioinformatics, Computational and Systems Biology

Re-validating Therapeutic Relevance of Monoclonal Antibody Therapies using Computerized and Digital Signal Processing-based Bioinformatics Approach

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Published Date: May 07,2020

Re-validating Therapeutic Relevance of Monoclonal Antibody Therapies using Computerized and Digital Signal Processing-based Bioinformatics Approach

Norbert Nwankwo1*, Ngozika Njoku2, Ignatius Okafor3, Michael Adikwu4, Emmanuel Ibezim4, Nnenna Ibezim5, and Mathew Azoji6

1Texans Can Academy-Dallas North, 9704 Skillman Street, Dallas, Tx 75243, USA

2Wilson N Jones Medical Center, 500 N. Highland Sherman, TX 75092, USA

3Dept of Pharm Tech and Pharmaceutics, University of Jos, Nigeria

4Dept of Pharm Tech and Pharmaceutics, University of Nigeria, Nsukka

5Dept of Computer ad Robotics Education, University of Nigeria, Nsukka

6Neimeth International Pharmaceuticals Plc, Lagoos, Nigeria


*Corresponding author: Norbert Nwankwo, Texans Can Academy-Dallas North, 9704 Skillman Street, Dallas, Tx 75243, USA, E-mail: 

Citation: Nwankwo N, Njoku N, Okafor I, Adikwu M, Ibezim E, et al. (2020). Re-validating Therapeutic Relevance of Monoclonal Antibody Therapies using Computerized and Digital Signal Processing-based Bioinformatics Approach. J Bioinf Com Sys Bio 2(1): 111.




Background: Monoclonal antibody therapies have emerged as the mainstay in the treatment of variety of ailments. As proteins with discrete amino acid residues, they qualify for computerized, sequence information-, bioinformatics- and digital signal processing-based assessments. These assessments include resistance resulting from single-point mutation S492R in the ectodomain of the Epidermal Growth Factor Receptor (EGFR). This mutation is known to abolish binding interaction with Cetuximab, hence reduced efficacy. However, Panitumumab’s binding interaction with EGFR and efficacies are unaffected.

Aim: In this study, the differences in efficacies of Cetuximab and Panitumumab arising from the acquired S493R mutation, which has clinically been verified, are computationally re-evaluated using Informational Spectrum Method (ISM) and two Amino Acid Indices (AAIs).

Methods: ISM is applied on the protein sequences of Panitumumab, Cetuximab and the target protein, Domain III of EGFR using two Amino Acid Scales, Electron-Ion Interaction Potential (EIIP) and CHOC760101. The protein sequences are converted into signals (numerical sequences) and further analyzed using Discrete Fourier Transform (DFT).

Results: The EIIP-based results revealed that the Cetuximabs (heavy- and light-chains) displayed 65.24 and 78.28% levels of affinity. On the other hand, Panitumumab, and the two variants of the target (EFGR and its mutant, EFGR S492R) showed highest degree of affinity (100%). In the case of CHOC760101, the Cetuximabs demonstrated higher activity than the Panitumumab while the mutant, S492R provided greater activity than the wild-type.

Discussions: Our findings signify that the Panitumumab, EFGR and its mutant, S492R offered greater binding interaction than the Cetuximabs. This is in agreement with the clinically verified findings. However, all the AAIs engaged in the interaction need be utilized in the study. This technique has earlier provided an opportunity to design drugs, vaccine, bioactive agents, bioinformatics tools, and biomedical devices such as Computer-Aided Drug Resistance Calculator (US20150370964).

Conclusions: This study therefore validates the applicability of this cost- and time-saving technique in the assessment, hence design and development of monoclonal antibody therapies. It also highlights the potential this technique has in reducing the financial burden on the patients who are already using expensive monoclonal antibody therapies. The validity of this technique rests on other sensitive technologies it has provided including Radar (flight control), Speech Detector (security).

Keywords: Cetuximab; Digital Signal Processing; Epidermal Growth Factor Receptor; Informational Spectrum Method; Monoclonal antibody; Panitumumab




Monoclonal antibody therapies belong to the group of biologic- or recombinant DNA technology-based agents (nucleic acids, proteins, whole cell, viral particles and vaccines), which are directed towards treatment of malignancies. They have now emerged as the major source of therapeutic interventions for numerous diseases including cancer, rheumatoid arthritis, rare diseases, etc., [1]. They are protein-based bio-molecules with very simply structures. As a result, they are devoid of complicated design and development procedures. This is unlike other agents such as alkaloids, steroids, etc. Monoclonal antibody therapies are basically amino acids in sequence.

Monoclonal antibodies therapies are largely employed in the treatment of diseases. For pharmaceutical industries, their engagement in the production and sales of monoclonal antibody therapies is anticipated to be financially rewarding [2]. By 2020, seventy monoclonal antibody therapies are envisaged to be in the market and their global sales are expected to rise to $125 billion [2]. As a result, engagement of these computerized, digital signal processing- and protein sequence information-based approaches has become eminent. These approaches will also benefit other bio-therapeutics [3]. The techniques have already profited the assessments of other protein-based agents (Enfuvirtide and Sifurvide) [4,5] as well as non-protein-based agents. Non-protein-based agents are known to have protein targets (e.g. Amprenavir) or genes (e.g. multi-drug resistance, MDR genes) encoding these bioactive agents [4,5].

There is a long list of monoclonal antibodies therapies currently approved by FDA, which are playing vital roles in disease management [2,6]. They include Cetuximab, Panitumumab, Trastuzumab Bevacizumab (Cancer), Adalimumab, Infliximab, (Rheumatoid Arthritis). Others are Bezlotoxumab (Enterocolitis), Reslizumab, Mepolizumab (Asthma), Olaratumab (Sarcoma), Daratumumab (Multiple myeloma), Raxibacumab (Anthrax infection), Idarucizumab (Hemorrhage), Denosumab (Osteoporosis), Ofatumumab (Leukemia), Natalizumab (Multiple sclerosis), Efalizumab (Psoriasis), Palivizumab (Respiratory Syncytial Virus (RSV). Necitumumab, Basiliximab, Muromonab-CD3 are employed on Transplantation Rejection, Rituximab and Sulesomab as suitably used in Diagnostic Imaging and Disease Detection [6].

Both Panitumumab and Cetuximab belong to the family of the FDA-approved monoclonal antibody therapies that target same protein, Domain III. Domain III belongs to the ectodomain (EC) of the Epidermal Growth Factor Receptor (EGFR). These monoclonal antibody therapies have extensively provided survival gains [7,8]. The ectodomain, also called the extracellular region of the EFGR (Figure 1) has 620 protein residues excluding the 24 amino acids that constitute the signal peptide [7]. This ectodomain consists of four domains. They are domain I (25-189), domain II (190-323), domain III (324-504), and domain IV (505-648) [9]. Recent report has shown that this single point mutation, S492R, destroys binding interaction that exists between the target protein and Cetuximab. As a result, Cetuximab is rendered less effective than Panitumumab [7]. Panitumumab is also reported to engage larger central cavity to escape the effect of the mutation [7].


Figure 1: Demonstrating the entire process, starting with the translation of the (A), Panitumumab sequence into a (B), Numerical Sequence or Signal using CHOC760101, its (C), plot, and (D) derived Fourier Transform-based Informational Spectrum.


According to a biophysical investigations carried out using Surface Plasmon Resonance (SPR) spectroscopy, the affinity observed between Panitumumab and domain III of the EGFR ectodomain is found to be twice greater than that of Cetuximab-EGFR [7]. Though affinity is reported to be a major Biophysical characteristic responsible for the differences in the efficacy of the monoclonal antibody therapies, other physio-chemical characteristics may be involved. As a result, variation in the Solvent accessible surface area buried in Panitumumab- and Cetuximab-EFGR, which may have contributed to the efficacy variations is in this study re-assessed. This variation is associated with lack of central cavity found in Cetuximab unlike the Panitumumab. Generally, both monoclonal antibody therapies are known to maintain negatively charge surfaces [7].

Apart from the S492R mutation found in the epitope of the EGF binding site, there are other newly identified mutations, which are not investigated in this study [7]. They include K467T, G465T, S464L and I491M. Out of these mutants, only K467T mutant demonstrated greater affinity for Panitumumab than Cetuximab. Studies show that K467T interact with other bio-molecules by disrupting salt bridge and providing stearic hindrance. Panitumumab, unlike the Cetuximab, lacks the big side-chain that usually provide for stearic hindrance. Computational re-assessments carried out in this study are limited to domain III of the EGFR ectodomain, which consists of the 181 protein residues of domain III (324-504).

We have preliminarily demonstrated the relevance of these computerized and digital signal processing-based bioinformatics procedures, which have provided the needed investigational apparatuses for diseases, therapeutic interventions and biomedical devices [10]. As a result, we advocated for the establishment of digital signal processing-based multi-disciplinary centers for effective utilization of these procedures [11]. We concluded that as sequence information-based procedures, these approaches are appropriate for the designing and assessing potencies, suitability and usability of bio-therapeutics including cancer [10,11], which in this study, is extended to monoclonal antibody therapies and other immuno-therapies. These therapies are basically proteins. Consequently, these translational processes are made possible through converting the alphabetic codes of the protein residues into signals (numerical sequences) and further analyzing them using digital signal processing techniques [12]. The techniques include Informational Spectrum Method [10-34] and Resonant Recognition Method [35-38].

The materials engaged in this study, include monoclonal antibodies, Panitumumab and Cetuximab as well as their target protein, the EFGR as well as two Amino Acid Indices (AAIs) [39] involved in the interaction. The AAIs are Electron-Ion Interaction Potential (EIIP) [10-39] and CHOC760101 [40]. EIIP is known to govern the inter-molecular interaction that first brings the molecules together prior to intra-molecular (physio-chemical or structural) interactions. EIIP is engagement in this study is paramount since it was reported that the S498R-induced resistance on Cetuximab came from the abrogated of the binding interaction with the target protein [27,35]. CHOC760101, which focuses on Solvent accessible surface area buried in protein, is also engaged. This is because Panitumumab is reported to engage central cavity to translate this single point mutation into improved potency [27,35]. Already, over 565 AAIs have been identified [42] and alterations in protein sequences of the protein-based agents, targets and genes have been recognized to involve one or more AAIs [41]. It is known that these alterations determine their bio-functionalities [40]. It is therefore pertinent that CHOC760101 and all other AAIs associated accessibility (e.g. JANJ780101) [41] be engaged to obtain the totality of the reduced susceptibility. This is expected to provide complimentary results.

We have preliminarily investigated similar bio-functionality. This include pharmacological effect (abrogation) caused by a single point mutation in nDARC (nDARC Y41F) [20,23,27]. We have also assessed resistance offered to various protein-based antiretroviral agents using more than one-point mutation. The agents include Fusion Inhibitors (Enfuvirtide and Sifurvirtide). Resistance offered by non-protein agents were evaluated using their target proteins. The agents consist of Darunavir (Protease Inhibitor), Raltegravir (Integrase Inhibitor), Bevirimat (Maturation Inhibitor) and Lamivudine (Nucleotide Reverse Transcriptase Inhibitor) [20,23,27].

This technique is known to be robust considering its engagement in the evaluation of several medical and biological processes in diseases such as HIV/AIDS [32], vasovagal syncope [43] cancer [10], Influenza [14, 16]. Pharmaceutical properties assessed using this technique includes pharmacological activities (drug resistance), therapeutic relevance of agents and vaccine candidates [22,27], as well as their suitability (efficacy/potency), and usability (druggability) [23]. More than 1000 proteins and functional groups have been investigated [35], and several biomedical devices including Computer-Aided Drug Resistance Calculator (USPTO Patent ID: US20150370964) created using this procedure [19]. Based on this approach, bioinformatics tools and programs such as ISTREE [44], innovative sequence-activity relationship (innovSAR) [30] have also been developed to ease assessment of bio-functionalities. This approach has been identified to revolutionize medical, pharmaceutical and biological processes [22].

Finally, it is interesting to note that the reliability of the digital signal processing procedure engaged here rests on the fact that it is based on Fourier Transform (FT). FT has already provided a technology that sustains flights in the air (Radar) [45]. In the subsequent section, the materials used in this study as well as a summary of the procedure, Informational Spectrum Methods are discussed.





Two Amino Acid Indices (AAIs) and four protein sequences are engaged. The sequences investigated include Panitumumab, Cetuximab (heavy and light chains, respectively), and the Epidermal Growth Factor Receptor (EGFR). Panitumumab sequence was obtained from a database [46]. Domain III of the ectoderm of the EFGR, which consists of 171 protein residues (334-504) [9], shown in Figure 4 was retrieved from a database [47]. Both heavy and light chains of Cetuximab are available in databases [47,48]. The corresponding values of the two AAIs employed in this study, Electron-Ion Interaction Potential (EIIP) and CHOC760101 are acquired from a database ( [39]. Table 1 is the single code representation of the 20 essential amino acids and their corresponding values for CHOC760101. All the sequences are as shown figures 2-4.

Table 1: Amino Acid Index of the CHOC760101, representing the “Nature of the Accessible and buried surfaces in Proteins” and showing the one letter alphabetic codes of the amino acids and their corresponding CHOC760101 values [39,40].



Figure 2: Amino Acid Sequence of Panitumumab (Protein ID: pdb|5sx4|H J Panitumumab 1ry).


Figure 3A: Amino Acid Sequence of Cetuximab, heavy chain chain (Source: DrugBank).


Figure 3B: Amino Acid Sequence of Cetuximab, light chain (Source: DrugBank)


Figure 4: Amino Acid Sequence of Epidermal Growth Factor Receptor (EFGR) (Protein ID: |P00533|1-670) highlighting the signal peptide (1-24), domain III (334-504) and the single point mutation (S492R) including the Signal Peptide (Source: Uniprot).


Experimental Procedure

A Discrete Fourier Transform-based Digital Signal Processing technique called Informational Spectrum Method (ISM) is engaged. This technique has been discussed severally [10-20,27,28] and detailed in [12,20]. However, a summary is provided here.

Informational Spectrum Method (ISM) procedure consists of four steps:

Translation of the Alphabetic Codes of the Protein residues into Signals: The alphabetic codes of the protein residues of Panitumumab, Cetuximab and the domain III of the ectoderm of EFGR are converted into numerical sequences (signals) using corresponding values of the EIIP and CHOC760101. In situations where several proteins from same family (e.g. gp120 of HIV or CD4 of several species of HIV host [20,32]) are involved, and they are of unequal lengths, up-sampling or zero-padding is carried preliminarily, bringing the residues to same window length. Zero-padding entails adding zero to the numerical sequence until same window length is achieved in the sequences. Though zero-padding has been reported to present problems [20,49], studies have shown that the process has no effect on the results [35].

Decomposition of the Signals using Discrete Fourier Transform (DFT): As signals, these proteins are processed using Discrete Fourier Transform (DFT). Fourier Transform refers to the transformation of the numerical sequences (signals) into frequency-based sinusoids with the aim of clarifying information entrenched in the signals [45].

Fourier Transform is expressed as:

                                                                    Equation 1


Where, e symbolizes exponential while w represents omega, and j is called an imaginary complex number, i.e. (-1)1/2 or √-1, and dt symbolizes changes in time.

When this transformation is applied to compartmentalized (digitalized or discrete) signals, it is referred to as Discrete Fourier Transform [45].

Mathematically, Discrete Fourier Transform is represented as:

                                                                    Equation 2


In this case, n is the discrete time index that runs from 0 to N-1 i.e. 0, 1, 2, . . . , N-1. k is the discrete frequency index and runs 0 to N/2 i.e. 0, 1, 2, . . . , N/2 as a mirror (symmetric) characteristics remain the product of the Discrete Fourier Transform (DFT) processing. x(k) represents m member of the numerical series where N is the length of the numerical series. X(n) stands for the coefficient of the DFT.


Informational Spectrum (IS): DFT application leads to complex numbers consisting of real and imaginary, and absolute values. Absolute values are engaged in determining the outcome of the analysis.

It is represented as:

                                                            Equation 3

Note: n=1,2,...,N/2 while R and I represent the real and imaginary parts respectively. “j” is already explained.

The degree of interaction, represented as the Absolute Spectrum is expressed as:

                                                     Equation 4


Note: Sa signifies Absolute spectrum of a given protein, while X(n) represents the DFT coefficient of the signal, X*(n) is the conjugate, and n stands for numbers 1-N/2.

Plots of the outcomes of the DFT-based analysis results are expressed in both x and y axes as positions and magnitudes of interaction, respectively. These plots are called Informational Spectra. To simplify the outcomes, the magnitudes of interaction are often normalized to 100% or 1 unit.

The entire process of translating protein sequences into Fourier Transform-based Informational Spectrum is demonstrated in figure 1 using Panitumumab sequence.

Common Informational Spectrum: To determine the common position of interaction, point-wise multiplication is carried out. Mathematically, this refers to element-wise multiplication within a space. In this case, the space is the sequence lengths [10,19,45].

This process, Common Informational Spectrum is expressed as:

                                                          Equation 5

Ca represents the absolute of the Common Spectrum Analysis while m = 1, 2, . . . M; and M is the number of protein/peptide sequences engaged. ∏ is the point-wise multiplication symbol.

Proteins with common biological functions are known to demonstrated prominent peak at the common position of interaction when point-wise multiplied [12,35].

Consensus Frequency (CF)

The common position of interaction, also called the Consensus Frequency is defined as:

                                                       Equation 6


Similarly, other positions or frequencies (f) representing peaks or amplitudes in the spectrum are represented as:

                                                     Equation 7




The outcomes of the experiments are presented in tables 2 and 3, as well as figures 5-9. The positions of remarkable amplitudes are obtained and converted to frequencies using equation number 7.


Table 2: EIIP-based results indicating Positions (P) and Domain Frequencies (f) of remarkable amplitudes (A) representing the level of affinity or binding interactions demonstrated by the monoclonal antibody therapies (Panitumumab and Cetuximab), and contributed by the target protein (EFGR).


Table 3: CHOC760101-based results indicating Positions (P) and Domain Frequencies (f) of remarkable amplitudes (A), which represent the participation of the accessible and buried surfaces in protein in the interactions by the monoclonal antibody therapies (Panitumumab and Cetuximab), and that, contributed by the target protein (EFGR).



Figure 5: (A) showing four remarkable EIIP-based amplitudes at positions (frequencies) of 11 (0.05), 26 (0.12), 73 (0.33) and 108 (0.49) and their respective amplitude (percentage binding interaction) as 1.57 (100%), 1.12 (71.33%), 1.40 (89.17%) and 1.53 (97.45%); and (B), four similar CHOC760101-based results with positions (frequencies) and amplitudes (% participation of the accessible and buried surfaces in protein) as 3 (0.014) and 643 (100%),11 (0.05) and 525 (81.65%) as well as 91 (0.41) and 546 (84.92%) for the monoclonal antibody therapy, for Panitumamb.


Electron Ion Interaction Potential (EIIP)-based Results

This parameter represents the level of affinity (binding interaction) that exists between proteins. As indicated in Tables 2 and Figures 5-9, the three monoclonal antibody therapies (Panitumumab, light- and heavy-chain Cetuximab), as well as their target protein (EFGR) and its mutant (S492R) shared remarkable amplitude (signifying high binding interaction) at about same domain frequency (f = 0.03-0.05). First, the Cetuximab (light chain) demonstrated a reasonable binding interaction (66.24%) at f = 0.03 (column 3). Secondly, at f = 0.04, Cetuximab (heavy chain) demonstrated 78.28% affinity. Thirdly, the two variants of the target proteins (EFGR and its mutant, EFGR S492R) and Panitumumab demonstrated 100% affinity for its targets at f = 0.04 and f = 0.05, respectively (column 3 and 4). The results show that Panitumumab and the two variants of the target proteins have greater willingness to bind than the Cetuximabs.


Figure 6: (A)  showing three remarkable EIIP-based amplitudes at positions (frequencies) of 19 (0.04), 90 (0.20) and 177 (0.4) and their respective amplitude (% affinity) as 1.72 (78.28%), 1.67 (75.90%) and 2.20 (100%); (B), four similar results using CHOC760101 with positions (frequencies) and amplitudes (% participation of the accessible and buried surfaces in protein) as 19 (0.04) and 1364 (100%), 85 (0.19) and 923 (67.67%), 196 (0.44) and 1076 (78.89%) as well as 200 (0.45) and 1207 (88.50%) for the monoclonal antibody therapy, Cetuximab Heavy chain.


Figure 7: (A)  showing four remarkable EIIP-based amplitudes at positions (frequencies) of 6 (0.03), 13 (0.06), 42 (0.20) and 97 (0.45) and their respective amplitude (% affinity) as 1.07 (65.24%), 1.14 (69.51%), 1.17 (71.34%) and 1.64 (100%); (B), four similar results using CHOC760101 with positions (frequencies) and amplitudes (% participation of the accessible and buried surfaces in protein) as 10 (0.047) and 671 (95.86%), 13 (0.06) and 647 (92.43%), 40 (0.19) and 620 (88.5%) as well as 93 (0.44) 700 (100%) for the monoclonal antibody therapy, Cetuximab light chain.


Figure 8: (A) showing five remarkable EIIP-based amplitudes at positions (frequencies) 6 (0.04), 32 (0.19), 69 (0.40), 73 (0.43) and 77 (0.45)  and their respective amplitude (% affinity) as 1.21 (100%), 1.11 (91.74%), 1.14 (94.22%), 1.12 (92.56%) and 1.14 (94.22%); (B), five similar results using CHOC760101 with positions (frequencies) and amplitudes (% participation of the accessible and buried surfaces in protein) as 9 (0.05) and 532 (69.73), 16 (0.09) and 558 (73.13%), 53 (0.31) and 763 (100%), 62 (0.36) and 708 (92.79%) as well as 44 (0.26) and 542 (71.04%) for the target protein, domain III of the ectoderm of EFGR.


Figure 9: (A) showing five remarkable EIIP-based amplitudes at positions (frequencies) of 6 (0.04), 32 (0.19), 69 (0.40), 73 (0.43) and 77 (0.45) and their respective amplitude (% affinity) as 1.21 (100%), 1.08 (89.26%), 1.15 (95.04%), 1.13 (93.39%) and 1.15 (95.04%); (B), five similar results using CHOC760101 with positions (frequencies) and amplitudes (% participation of the accessible and buried surfaces in protein) as 9 (0.05) and 540 (70.59%), 16 (0.09) and 542 (70.85%), 53 (0.31) and 765 (100%), 62 (0.36) and 714(93.33%) as well as 44 (0.26)  and 530 (69.28%) for the target protein, domain III of the ectoderm of EFGR MUTANT (S492R).


There are other common frequencies, which are identified in the experiment that may signify other bio-functionalities. For example, only the Cetuximabs (heavy and light chains) demonstrated reasonable activity (75.90 and 71.34%) at f = 0.2. Similarly, only the target proteins, EFGR (wide-type and mutant S492R) showed remarkable activity (94.22 and 95.04%) at f = 0.40 (columns 7 and 8). These two bio-functionalities need to be investigated further.

CHOC760101-based Results

This index represents “Nature of the accessible and buried surfaces in Proteins” [47] and examines the contributions of each protein residue in the central cavities in relation to their positions or availability for interaction in the cavity (accessibility). These results (Table 3 and Figures 5-8) show some distinguishable common frequencies with higher amplitude (activity) for agents (Cetuximab and Panitumumab), their target proteins (EFGR), and its mutant (S492R). These positions are found at frequencies, f = 0.04 and 0.05 (column 3).

At f = 0.04, the Cetuximab (heavy chain) demonstrated highest amplitude (100%), signifying highest accessibility of the amino acid residues to the protein surfaces. At f = 0.05, Panitumumab, Cetuximab (light chain), EFGR and its mutant, S492R revealed 81.65, 95.86, 69.73 and 70.59% accessibility, respectively.

CHOC760101-based experiment identified another frequency of interest, f = 0.44. At this frequency, only the Cetuximabs (heavy and light chains) showed high accessibility activity. The heavy chain recorded 78.89% while the light chain displayed 100%.




The Electron-Ion Interaction Potential (EIIP)-Based Findings

These outcomes revealed that at frequencies f = 0.03 to f = 0.05, the monoclonal antibody therapies (Panitumumab, heavy and light chains of Cetuximab), the target protein (domain III of EFGR) and its mutant (S492R) demonstrated remarkable affinity. Specifically, Panitumumab, domain III of EFGR and its mutant, S492R have greatest affinities of 100% while the Cetuximab (heavy and light chains) demonstrated lower affinities of 78.28 and 65.24%, respectively (columns 3 and 4). This computationally derived result is in accord with clinically derived results. According to a clinical report [7], Panitumumab is identified to have therapeutic advantage over Cetuximab as a result of its higher binding interaction with the target protein, domain III of the EFGR.

In this study also, it was revealed that EFGR and its mutant EFGR (S492R), which shared same originality demonstrated very high binding interaction (94.22% and 95.04%), respectively at same frequency, f=0.40. In the same manner, the Cetuximabs (heavy and light chains), which share same originality are found to shared remarkable affinities of 75.90% and 71.34%, respectively at same frequency, f = 0.20. This appears to attest to their sameness of originality. We have preliminarily validated this characteristic, using the CD4 of human and chimpanzee as well as various HIV isolates found across the Atlantic [20,32]. In these Informational Spectrum Method-based studies, the CD4 derived from both human and chimpanzee recorded highest degree of affinities (72.35%) and (76.40%), respectively at a frequency, f = 0.141 [20,32]. Several approaches including a comparative investigation of the pollical distal phalanges in fossil [50] as well as the DNA [51] have earlier been used to ascertain common originality of human and chimpanzee.

The CHOC760101-Based Findings

In the case of CHOC760101-based experiments, it was disclosed that the therapeutic agents (Panitumumab and Cetuximab), the target protein (domain III of EFGR) and its mutant (S492R) demonstrated remarkable activity at about same frequency (f=0.04 and 0.05). However, Panitumumab did not display any advantage. At this position, the mutant (S492R) demonstrated higher activity (70.59%) than the wild-type (69.73%). This result is in accord with clinically derived outcome, which recorded higher clinical effectiveness for the mutant than the wild-type [7]. Nonetheless, the Cetuximab (heavy and light chains) displayed greater activities (100 and 95.86%) than the Panitumumab (81.65%), indicating that the Panitumumab did not display any advantage using the CHOC760101-based experiments.

Another intriguing finding made using both Amino Acid Parameters (EIIP and CHOC760101) is that the two Cetuximabs (heavy and light chains) showed greatest number (4) of common frequencies (common position of interaction) with remarkable activities despite their significant differences in the amino acid lengths. In this regard, our findings indicated that using the CHOC760101-based experiments and at the frequency of 0.44, both heavy and light chains of Cetuximabs displayed significant activities (78.89 and 100%). Also at frequencies of 0.04 and 0.05, they demonstrated remarkable surface accessibilities of 100 and 95.86%, respectively. By means of the EIIP-based experiments, heavy and light chains of Cetuximabs displayed activities of 75.90 and 71.34%, respectively, at the frequency of 0.02, and also showed surface accessibilities of 78.28 and 71.34%, respectively at frequencies of 0.04 and 0.03. These outcomes are as shown in table 2 and 3.

It is important to note that the heavy and light chains of the Cetuximab have 449 and 214 protein residues, respectively, indicating a difference of 235 protein residues. This vast difference in sequence length notwithstanding, they share greatest number (4) of common frequencies with remarkable activities. This suggests sameness in originality. This finding is in accord with already established phenomenon and appears to confirm the reliability of the technique. Preliminarily, the understanding that bio-molecules with common biological functionalities demonstrate common positions of interaction though they may extensively vary in sequence length has been verified [20,31]. Similarly, the knowledge that CD4 of both human and chimpanzee, which have same originality, share highest activity (affinity) at a common frequency f = 0.141, and also maintained very high but different binding activity at a common frequency position, with which they interact with the HIV (0.0373) have also been verified [20,31].




Using an already validated Fourier Transform-, sequence information-based and computerized technique, we computationally re-evaluate the activities of two monoclonal antibody therapies (Panitumumab and Cetuximab), their target protein (EFGR) as well as its mutant (S492R). The results are found to re-establish that clinically derived outcomes including the EIIP-based findings revealed, which recognize that the monoclonal antibody therapy, Panitumumab, has greater affinity for the target protein (EFGR) than Cetuximabs (heavy and light chains) respectively. Similarly, the outcomes from the CHOC760101 show that the mutant (S492R), offered higher activity than the wide-type. This finding is also in accord with clinical results. On the other hand, the CHOC760101-based results indicated that Cetuximabs provided higher activities than the Panitumumab. Finally, our findings further authenticated common originality of the two chains of Cetuximabs despite vast variation in protein sequence. This study also recommends that all other available Amino Acid Indices be engaged in supplementary studies as they would provide complimentary outcomes. This investigation therefore appears to establish that this computerized, sequence- and Fourier Transform-based technique, which has earlier been shown to be revolutionizing the assessment of medical (disease such as HIV/AIDS, cancer), pharmaceutical (drug resistance) and biological processes as well as the designing and development biomedical devices, is employable in immunotherapies. The approach has the capacity to further reduce the financial burden already placed on patients who are on these novel therapeutic interventions. Fourier Transform-based technique is already known to be a valid technique, which has fetched other dependable technologies such as Radar (flight control), Speech Detector (security).




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