Application of median lethal concentration (LC50) of pathogenic microorganisms and their antigens in vaccine development



Lack of ideal mathematical models to qualify and quantify both pathogenicity, and virulence is a dreadful setback in development of new antimicrobials and vaccines against resistance pathogenic microorganisms. Hence, the modified arithmetical formula of Reed and Muench has been integrated with other formulas and used to determine bacterial colony forming unit/viral concentration, virulence and immunogenicity.


Microorganisms’ antigens tested are Staphylococcus aureus, Streptococcus pneumoniae, Pseudomonas aeruginosa in mice and rat, Edwardsiella ictaluri, Aeromonas hydrophila, Aeromonas veronii in fish, New Castle Disease virus in chicken, Sheep Pox virus, Foot-and-Mouth Disease virus and Hepatitis A virus in vitro, respectively. The LC50s for the pathogens using different routes of administrations are 1.93 × 103(sheep poxvirus) and 1.75 × 1010 for Staphylococcus aureus (ATCC29213) in rat, respectively. Titer index (TI) equals N log10 LC50 and provides protection against lethal dose in graded fashion which translates to protection index. N is the number of vaccine dose that could neutralize the LC50. Hence, parasite inoculum of 103 to 1011 may be used as basis for determination of LC50 and median bacterial concentrations (BC50).Pathogenic dose for immune stimulation should be sought at concentration about LC10.


Many countries have renewed effort towards development of vaccine against a number of infectious diseases, such as mastitis caused by Staphylococcus aureus in bovine and human [1]. Capsular polysaccharide, virulent antigens [2, 3] using adhesive proteins [4] as immunogenic derivatives, deoxyribonucleic acid (DNA), autolysin and protein-binding polysaccharides are also used to stimulate immune system [5,6,7]. However, Saganuwan reported toxicological basis of antidote [8] and a number of vaccines presently being developed is based on modified arithmetical method of Reed and Muench [9]. Hence numbers of colony forming units of some pathogenic bacteria, viruses and their antigens were determined, using median lethal concentrations (LC50s) established in laboratories, with intent to calculating immunogenic doses of various infectious agents.

Main text


Reference was made to journal articles on development of vaccines against methicillin resistance Staphylococcus aureus and other pathogenic microorganisms that cause diseases in human and animals. Median lethal concentrations (LC50s) of Staphylococcus aureus, Streptococcus pneumoniae, Pseudomonas aeruginosa in mice and rat, Edwardsiella ictaluri, Aeromonas hydrophila and Aeromonas veronii in catfish, New Zealand rabbit, fish and mice were translated to colony forming units. LC50 of in vitro cell cultures of hepatitis A virus and Foot and Mouth Disease virus were translated to LC1, whereas effective dose fifty (ED-50) for Newcastle Disease vaccines was translated to ED1 -in chickens [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]. The method of Reed and Muench [21] as modified by Saganuwan [9] was used for LC50 determination in various laboratories. Protection index (PI) is equal to titration index = Nlog10 LD50, whereas N is number of titration using vaccine. In vivo LD50 value can be replaced by tissue culture LD50 (TCL50).

Derivation of LD50 formula

  1. i.

    Modified formula of Reed and Muench

    \({\text{LD}}_{ 50} = \frac{MLD + MSD}{2}\) whereas MLD = Median lethal dose; MSD = median survival dose [9].

Derivation of LC50 formula

Conc. = initial concentration of colony forming unit per ml of sample = x

When concentration is double fold, triple fold and tetra fold, they are represented as 2 x X, 3 x X and 4 x X, respectively.

  1. ii.

    Hence, \({\text{LC}}_{ 50} = \frac{x + 2x + 3x + 4x}{10} x 5\)

    $${\text{LC}}_{ 50} = \frac{10x}{10} x 5$$
  2. iii.

    \({\text{LC}}_{ 50} = {\text{X}} \times 5\)

    x = initial concentration = colony forming unit; whereas LC50 = median lethal concentration that can kill 50% of test animals; x = initial concentration; multiplication factors for initial concentration = 10

  3. iv.
    $${\text{x}} = \frac{{LC_{50} }}{5}$$
  4. v.

    Number of colony forming unit (NCFU) per unit of sample [22]

    $${\text{NCFU}} = {\text{Nc}} \times {\text{Df}} .$$

    Nc = Number of colonies; Df = Dilution factor of the plate counted

  5. vi.

    Therefore \({\text{CFU}} = \frac{Nc x Df}{N}\)

    Substitute x for CFU in equation v

    \(\frac{{LC_{50} }}{5} = \frac{Nc x Df}{N}\)

    LC50 × N = 5(Nc × Df)

  6. vii.
    $${\text{LC}}_{ 50} = \frac{{5\left( {Nc x Df} \right)}}{N}$$
  7. viii.

    Median bactericidal concentration (MC50) formula is determined as follows

    \({\text{N}}_{\text{c}} = \frac{{N_{0} }}{{1 + e^{{r\left( {x - {\text{BC}}_{50} } \right)}} }}\) whereas N = Number of colonies for each plate.

  8. ix.
    $${\text{BC}}_{ 50} = \frac{No}{2}$$

    Thus 2BC50 could replace MBC

  9. x.

    \({\text{BC}}_{ 1} {\text{ = BC}}_{ 50} + \left[ {\frac{{l_{c} \left( {No - 1} \right)}}{r}} \right]\) whereas r = tangent slope on inflexion

    No could estimate the bactericidal intensity [23]

  10. xi.

    Since the rate of bacterial load depends on the concentration of neutrophils. Exponent = (− kp + g)t, where k is the second-order rate constant for bacterial killing, p = neutrophil concentration; g = first-order rate constant for bacterial growth; t = time.

    K = 2 × 10−8 ml per neutrophil per min; g = 8 × 10−3 min

  11. xii.

    When \({\text{P}} > \frac{g}{k}\) = critical neutrophil concentration

The critical neutrophil concentration = 3–4 × 105 per ml, a value of ≤ 5 × 105 predisposes human to bacterial infection [24]. All of the above formulas could be applied in determination of lethal concentration of immunogenic and anti-immunogenic agents in various models of vaccine development.


The colony forming unit, LC1-, median lethal concentration for each pathogenic microorganism, antigen, vaccine, animal model and their routes of administrations are presented in Table 1. The most virulent microorganism is Sheep Pox virus with LC50 value of 1.93 × 1010 cfu/ml followed by Edwardsiella ictaluri (2.8 × 104 cfu/ml), Streptococcus pneumonia(104–107 cfu/ml) and Staphylococcus being the least virulent in rat with IC50 of 1.75 × 1010 cfu/ml, using intradermal, intraperitoneal, intravenous and intraperitoneal route of administration, respectively. Sheep was most susceptible, followed by catfish, mice and rat being the least susceptible in the present study (Table 1).

Table 1 The estimated colony forming unit and  median lethal concentration (LD50) of pathogenic microorganisms’ antigens and vaccines


The median lethal concentration (1.1 × 108 CFU) for plasmid cloned neomycin (PC1 = Neo) and plasmid cloned neomycin methicillin resistance Staphylococcus aureus (PCl-Neo-MeccA) and 1 × 107 CFU for S. aureus fibrinogen in mice show that the microorganism is less virulent [5]. However, endotoxin-free phosphate buffered-saline (PBS) did not show lethality at 5 × 108 CFU [10]. The findings agree with the report indicating that active vaccination with a mixture of recombinant penicillin binding protein 2a in rabbit (rPBP2a/r) autolysin reduced mortality in methicillin resistant Staphylococcus aureus and protected mice against infection [7]. Higher level of autolysin specific antibodies has a predominant immune globulin G1 (lgG1) indicating that S. aureus is opsonized in serum of immunized mouse and could increase phagocytic killing [10]. But the lower concentration of New Castle Disease (NCD) Lasota (4.2–.6/ml) and 12 vaccine (5.7–9.6/ml) that offered protection against New Castle Disease may suggest robustness of the vaccines as compared to effective dose 50 (ED50) of B1 strain (5.1–20.9/ml), C30 strain (1.1–22/ml) and Villegas-Glisson University of Georgia (VG-VA) strain (0.3–16.2/ml), respectively [11]. But pneumococcal surface protein A (PspA3+2) is better than PspA2+4 and PspA2+5 vaccine in respect of cross protection against pneumococcal infection [13]. The conjugated α helical region of PspA to Vi enhanced protective immune response and provided protection against pneumococcal infection [14]. Antibody elicited by PspA recombinant protein and DNA vaccine proffer humoral response which is different from fragment crystallizable (Fc), (lgG1/lgG22 ratios) and fragment antigen-binding (Fab) epitopes of the induced antibodies [22]. The tissue culture lethal dose 50 (TCLD50) determined by Cormier and Janes showed that zeolite could be used against hepatitis A virus infection [12]. Foot and mouth disease (FMD) titer of serotype A, O and SAT-2 from the roller cultivation system provided protection at 2 weeks post-vaccination [15]. The LC50 of S. aureus (1.75 × 1010 cfu/ml) and P. aeruginosa (3.0 × 108 cfu/ml) show that the microorganisms are less virulent [16]. The pathogenicity is based on clinical signs, survivability and postmortem changes of the infected animal. Therefore, the LC50 of 1.93 × 103 shows that the intradermal Romanian SPPV is a potent vaccine for control and prevention of sheep pox in a disease-free or endemic country [17]. Edwardsiella ictaluri is moderately pathogenic in Pangasionodon hypophthalamus with LC50 of 2.8 × 104 cfu/ml and caused necrosis of liver and haemolysis [18]. Vaccination against A. hydrophila using glycoproteins (5 × 109 cfu/ml) with ginseng, provided reliable immunity in fish and rabbit [19], though the immunity may not be strong. Bacteriovorax strain H2 is relatively safe in mammalian bio system including snakehead and could be used as a probiotic agent for the bio control of A. veronii infection in snakehead [20]. As a number of promising protein-based and whole cell vaccines are currently undergoing different phases of development [29], microorganisms and antigens with lower LC50 values are more pathogenic and may require higher doses of vaccines. More so, different bacteria have different incubation periods and mixed infection decrease incubatory period and longevity of the host [22]. Pathogenicity is multifactorial with genetic regions associated with virulence and resistance determinants. Although pathogenicity islands (PAIs) and resistance islands (RIs) play great role in bacterial infection [25]. Pathogenicity Island (150-kb) encodes several genes for pathogenesis and antibiotic resistance [26]. Therefore pathogenicity is qualitative whereas virulence is quantitative [27]. Pathogenicity islands are acquired by horizontal gene transfer that promote genetic variability described as evolution quantum leaps involving large amounts of DNA [28]. Mechanisms of pathogenicity are via lysis of cell wall, toxin, adhesins and invasion of host cell [29]. Application of monitoring programs, prudent use of guidelines and campaigns could minimize the transmission and spread resistant bacteria [30, 31]. Pathogenic potential of microbes is a continuous phenomenon [32] that is related to infective dose and virulence [33]. Hence, host–pathogen parameters give progression of infection and may lead to survival or death [34]. But sometimes cell lines are used and the information related to intercellular mechanism is lacking [35], making it difficult to predict ideal pathogenicity/virulence, most especially in in vitro-in vivo translation. However, molecular basis of pathogens has made possible, identification of many therapeutic interventions [36], as evidenced by disease-gene-drug interaction [37], during the late stage of new antibiotic development. This can help pharmaceutical companies that have limited resources to discover and develop new antibiotics [38] for emerging and rare diseases that may need orphan drugs [39].

Determination of pathogenicity using a revised arithmetical method of Reed and Munch [9] is an application of computational biology, which is the science of using biology to develop algorithms or models for understanding biological relationship [40] that involves data analysis and interpretation [41]. Using heterogeneity of animal models in the present study and the data generated, pose a special challenge [42], which could be summarized by expanding the computation that would find a range of value, which would serve as basis for determination of one or more biological parameters [43]. In the present study, the LC50 of pathogenic microorganisms, antigens and titrated antibodies should be sought between 1.93 × 103 and 1.75 × 1010 CFU/ml depending on the in vitro or in vivo test models, route of inoculation and pathogenicity of the test pathogen, antigen and titrated antibody [44]. Computational immunology may translate to the possibility of all mammals having homogeneity of immunogenes from evolution [45]. Data derived from complex processes driven by evolution [46], and deep learning methods as complicated by powerful programmed machine with improved software infrastructures, may not provide ultimate solution for the field of computational biology [47], making the present study very relevant.

Diversity of quasispecies predicts a limit between mutation rate, population dynamics and pathogenesis [48] via mathematical modeling, that may produce results similar to hypothetical and real experiments [49]. The locus that determines pathogenicity may be involved in lipopolysaccharide biosynthesis [50]. Also, pathogenicity of a microbe varies with the genetic background of mouse strain [32]. The strategies used by pathogenic bacteria to cause pathogenicity are via cell wall, toxins, adhesins, invasion, intracellular lifestyles, regulation of virulence factor, evolution of bacterial pathogen, antibacterial resistance, pathogen-innate immune system interaction and viability of complete genome sequences [29]. But the evolution of pathogenicity is based on traits that ensure survival of microorganisms in their habitats [51]. Different pathogenic microbes isolated from host species have different incubation period. But when there is mixed infection, the incubation period decreases [22]. The pathogenicity index of 100µ per 106 cfu may be applied for screening of P. multocida [52]. Influenza virus can affect colonization of S. pneumoniae, S. aureus, N. meningitidis, M. tuberculosis, and S. pyogenes, RSV, Rhinovirus and HPIV. This has been proven by various mathematical models of microbial pathogenicity [53].


  • The study was based on data generated in various laboratories; hence standard operating procedure (SOP) and general lab practice (GLP) may affect our findings.

  • Differences in formulas may also affect the data generated.

  • Routes of administration, animal models and variation in pathogenic molecules may affect the data generated.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.


LC50 :

Median lethal concentration

LD50 :

Median lethal dose

BC50 :

Median bacterial concentration


Number of vaccine dose

T1 :

Titre index

LC10 :

Lethal concentration 10


Initial concentration


Median lethal dose


Median survival dose


Number of colonies forming unit


Number of colony


Dilution factor




Tangent slope on inflexion


Second order rate constant


Neutrophil concentrations


First order constant for bacterial growth


Time taken to grow


Sheep Pox virus


Hepatitis A virus


phosphate buffered-saline


Plasmid cloned neomycin


Plasmid cloned neomycin methicillin Staphylococcus aureus

IgG1 :

Immunoglobulin G1


Recombinant penicillin binding protein 2a in rabbit


New Castle disease

ED50 :

Effective dose 50

TCLD50 :

Tissue culture median lethal dose


Pneumococcal surface protein A


Fragment crystallizable


Fragment anti-gen binding


Villegas-Glisson, University of Georgia


Foot-and-Mouth Disease


  1. 1.

    Fattom AI, Horwith G, Fuller S, Propst M, Naso R. Development of StaphVAX, a polysaccharide conjugate vaccine against S. aureus infection: from the lab bench to phase III clinical trials. Vaccine. 2004;22:880–7.

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Shinefield H, Black S, Fattom A, Horwith G, Rasgon S, Ordonez J, Yeoh H, Law D, Robbins JB, Schneerson R, Muenz L, Fuller S, Johnson J, Fireman B, Alcorn H, Naso R. Use of a Staphylococcus aureus conjugate vaccine in patients receiving hemodialysis. N Engl J Med. 2002;346:491–6.

    PubMed  Article  Google Scholar 

  3. 3.

    Weichhart T, Horky M, Sollner J, Gang S, Henics T, Naggy E, Meinke A, von Gabain A, Fraser CM, Gill SR, Hafner M, von Ahsen U. Functional selection of vaccine candidate peptides from Staphylococcus aureus whole-genom expression libraries in vitro. Infect Immun. 2003;71(8):4633–41.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Kalyvaja R. Staphylococcus aureus and Lactobacillus crispatus. Adhesive characteristics of two gram-positive bacterial species. Ph.D. Microbiology, University of Helsinki. 2006.

  5. 5.

    Senna JPM, Roth DM, Oliveira JS, Machado DC, Santos DS. Protective immune response against methicillin resistant Staphylococcus aureus in ma murine model using a DNA vaccine approach. Vaccine. 2003;21:2661–6.

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Gaudreau MC, Lecasse P, Talbot BG. Protective immune response to a multi-gene DNA vaccine against Staphylococcus aureus. Vaccine. 2007;25:814–24.

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Haghighat S, Siyadat SD, Sorkhabadi SMR, Sepahi AA, Mahdavi M. Clonnig, expression and purification of autolysin from methicillin-resistant Staphylococcus aureus: potency and challenge in Balb/c mice. Mol Immunol. 2017;8:10–8.

    Article  CAS  Google Scholar 

  8. 8.

    Saganuwan SA. Toxicity: the basis for development of antidotes. Toxicol Open Access. 2015;1(1):1–2.

    Google Scholar 

  9. 9.

    Saganuwan SA. A modified arithmetical method of Reed and Muench for determination of median lethal dose (LD50) Afr. J Pharm Pharmacol. 2011;5(11):1543–6.

    Google Scholar 

  10. 10.

    Haghighat S, Siadat SV, Sorkhabadi SMR, Sepahi AA, Sadat SM, Yazdi MH, Mahdavi M. Recombination PBP2G as a vaccine candidate against methicillin-resistant Staphylococcus aureus: immunogenicity and protectivity. Microbiol Pathol. 2017;108:32–9.

    CAS  Article  Google Scholar 

  11. 11.

    Boumart Z, Hamdi J, Daouam S, Elakarm A, Tadlaoui KO, El Harrak N. Thermal stability study of five New Castle disease attenuated vaccine strains. Avian Dis. 2016;60:779–83.

    PubMed  Article  Google Scholar 

  12. 12.

    Cormier J, Janes N. Concentration and detection of Hepatitis A virus and its indicator from artificial seawater using zeolite. J Virol Method. 2016;235:1–8.

    CAS  Article  Google Scholar 

  13. 13.

    Piao Z, Akeda T, Takeuchi O, Ishii KJ, Ubukata K, Briles DE, Tomono K, Chishi K. Protective properties of a fusion pneumococcal surface protein A (PSPA) Vaccine against pneumococcal challenge by five different PSPA clades in mice. Vaccine. 2014;32:5607–13.

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Kothari N, Kothari S, Choi TY, Dry A, Briles DE, Rhee DK, Carbish R. A bivalent conjugate vaccine containing PSPA families 1 and 2 has the potential to protect against a wide range of Streptococcus pnuemoniae strains and Salmonella typhi. Vaccine. 2015;33:783–8.

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Al Hassan. Effect of different culture systems on the production of Foot-and-Mouth Disease trivalent vaccine. Vet World. 2016;9(1):32–7.

    Article  Google Scholar 

  16. 16.

    Jankie S, Lenelle J, Suepaul R, Pereira LP, Akpaka P, Adebayo AS, Pillai G. Determination of the infective disease of Staphylococcus aureus (ATCC 27853) and Pseudomonas aeruginosa (ATCC 27853) when injected intraperitoneally in Sprague dawley rats. Br J Pharm Res. 2016;14(1):1–11.

    Article  Google Scholar 

  17. 17.

    Boumart Z, Daouawm S, Belkourati I, Rafi L, Tuppurainen E, Tadkoli KO, El Harrak M. Comparative innocuity and efficiency of live and inactivated sheep vaccines. BMC Vet Res. 2016;12(133):1–6.

    Google Scholar 

  18. 18.

    Susanti W, Indrawati A, Pasaribu FH. Kajiampatogenisitasbakteri Edwards iellaictaluri Padaikan Patin Pangosion odonhypothalamus. J Akuakult Indonesia. 2016;15(2):99–107.

    Article  Google Scholar 

  19. 19.

    Ciftci A, Onuk EE, Ciftci G, Findik A, Sogut MU, Didinen BI, Aksou A, Ustunakin K, Gulhan T, Balta F, Altun S. Development and validation of glycoprotein protein-based native-subunit vaccine for fish against Aeromonas hydrophila. J Fish Dis. 2016;39:981–92.

    CAS  PubMed  Article  Google Scholar 

  20. 20.

    Cao H, Hou S, Lu L, Yang X. Identification of a bacteriovovax sp. isolate as a potential biocontrol bacterium against snakehead fish pathogen Aeromonas Veronii. J Fish Dis. 2014;37:283–9.

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Reed LJ, Muench H. A simple method of estimating fifty percent endpoint. Am J Epidemiol. 1938;27:493–7.

    Article  Google Scholar 

  22. 22.

    Sharma P, Sihag RC. Pathogenicity test of bacterial and fungal fish pathogens in Cirrihinus madrigals infected with EUS disease. Pak J BiolSci. 2013;16(20):1204–7.

    Article  Google Scholar 

  23. 23.

    Liu YQ, Zhang YZ, Gao PJ. Novel concentration killing curve method for estimation of bactericidal potency of antibiotics in an invitro dynamic model. Antimicrob Agent Chemother. 2004;48(10):3884–91.

    CAS  Article  Google Scholar 

  24. 24.

    Li Y, Katlin A, Like JD, Silverstem SC. A critical concentration of neutrophils is required for effective bacterial killing in suspension. Proc Natl Acad Sci. 2002;99(12):8289–94.

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Yoon SH, Park Y-K, Kim JF. Exploration and analysis of pathogenicity and resistance islands. Nucleic Acid Res. 2015;43:624–30.

    Article  CAS  Google Scholar 

  26. 26.

    McBride SM, Coburn PS, Baghdayan AS, Williams RJL, Grand MJ, Shankar N, Gilmore MS. Genetic variation and evolution of the pathogenicity island of Enterococcus faecalis. J Bacteriol. 2009;191(10):3392–402.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Shapeiro-Ian D, Fuxa JR, Lacey LA, Onstad DW, Kaya HK. Definitions of pathogenicity and virulence in invertebrate pathology. J Invert Pathol. 2005;88:1–7.

    Article  Google Scholar 

  28. 28.

    Hentschel U, Hacker J. Pathogenicity islands: the tip of iceberg. Microb Infect. 2001;3:545–8.

    CAS  Article  Google Scholar 

  29. 29.

    Wilson JW, Schurr MJ, LeBlanc CL, Ramamurthy R, Buchanan KL, Nickerson CA. Mechanisms of bacterial pathogenicity. Postgrad Med J. 2002;78:216–24.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Saga T, Yamaguchi K. History of antimicrobial agents and resistance bacteria. JMAJ. 2009;52(2):103–8.

    Google Scholar 

  31. 31.

    Mctwen SA, Fedorka-Cray PJ. Antimicrobial use and resistance in animals. CID. 2002;34(3):93–106.

    Article  Google Scholar 

  32. 32.

    Casadevall A. The pathogenic potential of a microbe. Msphere. 2017;2(1):1–7.

    Article  Google Scholar 

  33. 33.

    Legget HC, Cornwallis CK, West SA. Mechanisms of pathogenesis, infective dose and virulence in human parasites. PLoS Pathog. 2012;8(2):1–5.

    Google Scholar 

  34. 34.

    Duneau D, Ferry J-B, Rerah J, Kondoff H, Ortiz GA, Lazaro BP, Bouchon N. Stochastic variation in the initial phase of bacterial infection predicts the probability of survival in D. melanogaster. Life. 2017;6:e28298.

    Google Scholar 

  35. 35.

    Law GL, Tisoncik-Go J, North MJ, Katie MG. Drug repurposing: a better approach for infectious drug discovery? Curr Opin Immunol. 2013;25(5):585–92.

    Article  CAS  Google Scholar 

  36. 36.

    Groft SC. Rare diseases research expanding collaborative translational research opportunities. CHFST. 2013;144(1):16–23.

    Google Scholar 

  37. 37.

    Tasleem M, Ishrat R, Islam A, Ahmad F, Hassan MI. Human disease insight: an integrated knowledge-based platform for disease-gene-drug information. J Infect Publ Health. 2016;9:331–8.

    Article  Google Scholar 

  38. 38.

    Fernandes P, Martens E. Antibiotics in late clinical development. Biochem Pharmacol. 2017;133:152–63.

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Aronson JK. Rare diseases and orphan drugs. Br J Clin Pharmacol. 2006;61(3):243–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Vadesitho CFM, Ferreira DN, Moreno AT, Chavez-Olortegui G, de Machado Avila RA, Oliveira MLS, Ho PL, Miyaji EN. Characterization of the antibody response elicited by immunization with pneumococcal surface protein A (PspA) as recombinant protein or DNA with Streptococcus pneumonia. Microb Pathol. 2012;53:243–9.

    Article  CAS  Google Scholar 

  41. 41.

    Saganuwan SA. The new algorithm for determination of median lethal dose fifty (LD50)and effective dose fifty(ED50) for snake venom and antivenom in mice. Int J Vet Sci Med. 2015;4(3):1–4.

    Google Scholar 

  42. 42.

    Loman N, Watson M. Do you want to be a computational biologist? Nat Biotechnol. 2013;37(11):996–8.

    Article  CAS  Google Scholar 

  43. 43.

    Nusinov R, Bonhoeffor S, Papin JA, Sporns O. From, “what is” to “what isn’t?” computational biology. PLoS Comput Biol. 2015;11(7):1–3.

    Google Scholar 

  44. 44.

    Markowety F. All biology is computational biology. PLoS Biol. 2015;15(3):1–4.

    Google Scholar 

  45. 45.

    Berger B, Daniel NM, Yu YW. Algorithm advances take advantages of the structure of massive biological data landscape. Commun Can. 2016;59(8):71–8.

    Google Scholar 

  46. 46.

    Angermueller C, Parnamae T, Parts L, Stegle O. Deep learning of computational biology. MolSyst Biol. 2016;12(878):1–16.

    Google Scholar 

  47. 47.

    Feldman C, Anderson R. Review: current and new generation pneumococcal vaccines. J Infect. 2014;69:309–25.

    PubMed  Article  Google Scholar 

  48. 48.

    Vignuzzi M, Stone JK, Arnold JJ, Cameron CE, Andino R. Quasi species diversity determines pathogenesis through cooperative interactions within a viral population. Nature. 2006;439(7074):344–8.

    CAS  PubMed  Article  Google Scholar 

  49. 49.

    Garcia J, Shea J, Alvarez-Vasquez F, Qureshi A, Luberto C, Voit EO, Poeta MD. Mathematical modelling of pathogenicity of Cryptococcus neorformans. MolSystBiol. 2008;183:1–13.

    Google Scholar 

  50. 50.

    Dow JM, Osbourn AE, Wilson TJG, Daniels MJ. A locus determining pathogenicity of Xanthomonas campestris is involved in lipopolysaccharide biosynthesis. MPMI. 1995;8(5):766–77.

    Article  Google Scholar 

  51. 51.

    Van Baarlen P, van Belkum A, Summerbell RC, Crous PW, Thomma PHJ. Molecular mechanisms of pathogenicity: How do pathogenic microorganisms develop cross-kingdom host jumps? FEMS Microbiol Rev. 2007;31:239–77.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. 52.

    Pilatti RM, Furian TQ, Lima DA, Finkler F, Brit BG, Salle CTP, Morae HLS. Establishment of a pathogenicity index of one-day-old broilers to Pasteurella multocida strains isolated. Braz J Poult Sci. 2013;37(11):344–8.

    Google Scholar 

  53. 53.

    Opatowski L, Baguelin M, Eggo RM. Influenza interaction with circulating pathogens and its impact on surveillance, pathogenesis and epidemic profile: a key role for mathematical modeling. PLoS Pathog. 2017.

    Article  Google Scholar 

Download references


I sincerely thank Williams Yusuf of Federal University of Agriculture Makurdi and Kehinde Ola Emmanuel of National Open University all in Nigeria for typing the work.


The study was carried out using my monthly emoluments.

Author information




SAS designed and carried out the study, analyzed the data, wrote and proof read the manuscript. The author read and approved the final manuscript.

Corresponding author

Correspondence to Saganuwan Alhaji Saganuwan.

Ethics declarations

Ethics approval and consent to participate

Not applicable, because neither animals nor humans were used for the study; the data were generated from laboratories.

Consent to publish

Not applicable.

Competing interests

The author declares that he has no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Saganuwan, S.A. Application of median lethal concentration (LC50) of pathogenic microorganisms and their antigens in vaccine development. BMC Res Notes 13, 289 (2020).

Download citation


  • Vaccine
  • Pathogenicity
  • Model
  • Arithmetic
  • Development
  • Colony forming unit