Background: There has been a significant increase in the cost and timeline of delivering new drugs for clinical use over the last three decades. Despite the increased investments in research infrastructure by pharmaceutical companies and technological advances in the scientific tools available, efforts to increase the number of molecules coming through the drug development pipeline have largely been unfruitful. Main body: A non-systematic review of the current literature was undertaken to enumerate the various strategies employed to improve the success rates in the pharmaceutical research and development. The review covers the exploitation of genomics and proteomics, complementarity of target-based and phenotypic efficacy screening platforms, drug repurposing and repositioning, collaborative research, focusing on underserved therapeutic fields, outsourcing strategy, and pharmaceutical modeling and artificial intelligence. Examples of successful drug discoveries achieved through application of these strategies are highlighted and discussed herein. Conclusions: Genomics and proteomics have uncovered a wide array of potential drug targets and are facilitative of enhanced scrupulous target identification and validation thus reducing efficacy-related drug attrition. When used complementarily, phenotypic and target-based screening platforms would likely allow serendipitous drug discovery while increasing rationality in drug design. Drug repurposing and repositioning reduces financial risks in drug development accompanied by cost and time savings, while prolonging patent exclusivity hence increased returns on investment to the innovator company. Equally important, collaborative research is facilitative of cross-fertilization and refinement of ideas, while sharing resources and expertise, hence reducing overhead costs in the early stages of drug discovery. Underserved therapeutic fields are niche drug discovery areas that may be used to experiment and launch novel drug targets, while exploiting incentivized benefits afforded by drug regulatory authorities. Outsourcing allows the pharma industries to focus on their core competencies while deriving greater efficiency of specialist contract research organizations. The existing and emerging pharmaceutical modeling and artificial intelligence softwares and tools allow for in silico computation enabling more efficient computer-aided drug design. Careful selection and application of these strategies, singly or in combination, may potentially harness pharmaceutical research and innovation.

Although cervical cancer is preventable, it is still the second leading cause of cancer deaths among women in the world. Further, it is estimated that around 5–10% of hospital admissions are due to drug related problems (DRPs), of which 50% are avoidable. In cancer therapy, there is an immense potential for DRPs due to the high toxicity of most chemotherapeutic regimens. Hence, this study sought to assess DRPs among patients with cervical cancer at Kenyatta National Hospital (KNH). A cross-sectional study was conducted at the oncology units of KNH. A total of 81 study participants were recruited through simple random sampling. Data were collected from medical records and interviewing patients. The appropriateness of medical therapy was evaluated by comparing with National Compressive Cancer Network and European Society for Medical Oncology practice guideline of cervical cancer treatment protocol. The degree of adherence was determined using eight-item Morisky medication adherence scale. The likelihood of drug interaction was assessed using Medscape, Micromedex and Epocrates drug interaction checkers. The data were entered in Microsoft Excel and analysed using statistical software STATA version 13.0. Descriptive statistics such as mean, percent and frequency were used to summarise patients’ characteristics. Univariable and multivariable binary logistic regression were used to investigate the potential predictors of DRPs. A total of 215 DRPs were identified from 76 patients, translating to a prevalence of 93.8% and a mean of 2.65 ± 1.22 DRPs. The predominant proportion of DRPs (48.2%) was identified in patients who had been treated with chemoradiation regimens. Adverse drug reactions 56(69.1%) and drug interactions 38(46.9%) were the most prevalent DRPs. Majority (67.9%) of the study population were adherent to their treatment regimens. Forgetfulness 18(69.2%), expensive medications 4(15.4%) and side effects of medications 4(15.4%) were the main reasons for medication non-adherence. Patients with advanced stage cervical cancer were 15.4 times (AOR = 15.4, 95% CI = 1.3–185.87, p = 0.031) more likely to have DRPs as compared to patients with early stage disease. Adverse drug reactions, drug interactions, and need of additional drug therapy were the most common DRPs identified among cervical cancer patients. Advanced stage cervical cancer was the only predictor of DRPs.

Background. The 2013 Global Burden of Disease report indicated that 80% of stroke deaths occur in low- and middle-income regions. Although stroke has been consistently reported as one of the three leading causes of morbidity and mortality in the past years in Ethiopia, there is a paucity of data regarding treatment outcomes of stroke if sufficient. Hence, the present study aimed to assess patterns of treatment outcomes and associated factors among hospitalized stroke patients at Shashemene Referral Hospital. Methods. A retrospective cross-sectional study was conducted at the medical ward of Shashemene Referral Hospital. A total of 73 hospitalized stroke patients during the period 2012–2017 were included in the study. Demographic characteristics, risk factors, and stroke types and their hospital outcomes were reviewed from the medical records of the patients. The data were entered and analyzed using SPSS version 16.0. Descriptive statistics such as percent and frequency were used to summarize patients’ characteristics. Binary logistic regression was used to investigate the potential predictors of treatment outcome. A p-value ≤0.05 was considered statistically significant. Result. Ischemic stroke was the most common type of stroke (65.8%) diagnosed in our setting. Hypertension (52.05%) was the common comorbid condition. More than half (54.79%) of the stroke patients improved on treatment. Dyslipidemics were prescribed to 68.49% of patients and the most popular antiplatelet was aspirin, which was prescribed to 61.64% of the study participants. Age, sex, type of stroke, and type of comorbidity were not significant factors of stroke treatment outcome. Conclusion. Ischemic stroke was the most common type of stroke diagnosed among the study participants while aspirin and statins were the most frequently used drugs in the management of stroke. Approximately 50% of hospitalized stroke patients had good treatment outcome and none of the investigated variables were significantly associated with the treatment outcomes.